Using Context Inference to Improve Sentence Ordering for Multi-document Summarization

In this paper, we propose a novel context inference-based approach for sentences ordering in mult i-document summarization application. Our method first detects whether or not two summarizat ion sentences should be adjacent according to the similarity between one summarizat ion sentence and the context of the other one, and then it co mputes the reliability of the adjacent summarization sentences based on the similarity and their relative position. To be specific, the first sentence will be selected according to features of sentence, and the second sentence will be selected if and only if it has the maximu m reliability with previous sentence. Evaluation result shows that our method outperforms the state-of-the-art ones on DUC2004 corpus.

Introduction
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. Whereas, sentence ordering, the last task of multi-document summarization, will finally affect the quality of summarization. Furthermore, the task of sentence ordering in multi-document summarization is harder than that in single document summarization because multiple documents are created by different authors who have different writing styles and backgrounds. Therefore, no natural order of texts can be extracted as the basis of sentence ordering judgment. How to conduct an efficient and effective method for sentences ordering is a difficult but important task for both multidocument summarization and other text processing job, e.g. Question Answering.
Currently, a variety of studies have been reported on sentence ordering. Some methods adopted chronological information (McKeown et al., 1999;Lin et al., 2001;Barzilay et al., 2002;Okazaki et al., 2004) while others learned the natural order of sentences from source documents or large corpus (Lapata, 2003;Barzilay and Lee, 2004;Nie et al., 2006;Ji and Nie, 2008). However, chronological information cannot be easily extracted from those non-news documents and constructing a large corpus also is not so easy. Furthermore, those results of all above methods are far from satisfactory. Therefore, how to achieve coherent summarization still is an issue for us. This paper proposes a novel method to infer the order of summarization sentences for multidocument summarization according to their context. We first judge whether or not two summarization sentences should be adjacent based on the similarity between one summarization sentence and the context of the other one, and then compute the reliability based on the similarity and relative position of the adjacent summarization. To be specific, the first sentence will be selected based on its features, and the second sentence will be selected if and only if it has the maximum adjacency reliability with previous sentence. The experimental results on DUC2004 corpus show that our method outperforms state-of-the-art ones.
The reminder of this paper is organized as follows. In Section 2, the related work is introduced. The motivation of our method is discussed in Section 3. In Section 4, the context inference-based sentence ordering algorithm is described. In Section 5, the experiments and evaluation are presented. Conclusions and future work are given in Section 6.

Related Work
So far many studies on sentence ordering have been proposed. They can be divided into two categories: chronology-based method and natural order-based method. To be specific, chronologybased methods determine sentence ordering according to the chronological order of events while natural order-based methods infer sentence ordering according to some cues that are learned from source documents and corpus. Barzilay et al. (2002) proposed a chronologybased method to sort sentence, they assumed the theme of sentences were the cues of sentence ordering. Based on it, they presented a strategy according to the first published date of the theme; then sorted sentences based on their order of presentation in the same article. Okazaki et al. (2004) proposed an approach to improve the chronological sentence ordering method by using precedence relation technology. They assumed the presupposed information should be transferred to reader in advance before the sentence was interpreted. They first arranged sentences in a chronological order and then estimated the presupposed information for each sentence by using the content of the sentences located in before each sentence in its original article.
Generally speaking, the articles in news domain usually contain descriptions of date and events accompanying the publication sequences. Therefore, chronology-based method is practical for news domain. However, chronological information is not ubiquitous in a large number of multi-document summarization tasks. So, some studies began to focus on general domain, and they sorted sentences according to the source documents and corpus. McKeown et al. (2001) and  presented a majority ordering algorithm to sort sentences. They classified sentences of source documents into different themes or topics using summarization sentences. If sentences from theme 1 preceded sentences from theme 2 when they appeared in same text, then putted theme 1 was preceding theme 2. The order of summarization sentences was determined by the order of themes. However, there were some potential issues in this kind of method. Firstly, it was not easy to correctly cluster sentences into topics. Secondly, some summarization sentences may belong to a same topic. Finally, the relative order between two topics may be not steady. Lapata (2003) provided an unsupervised probabilistic model for sentence ordering. The model assumed that sentences were represented as a set of features that could be automatically extracted from the corpus without manual annotation. The conditional probability of sentence pairs can be learned from a training corpus. By computing conditional probability of each sentence pairs, the approximate optimal global sentence ordering can be achieved using a simple greedy algorithm. Bollegala et al. (2005) combined three ordering methods together, said chronological ordering, probabilistic ordering and topic relatedness ordering, and adopted a machine learning approach for sentence ordering. The mixed system can achieve better performance than any of the three individual one. To be extended, they also proposed a bottom-up approach for sentence ordering (Bollegala et al., 2010). They defined four criteria (chronology, topical-closeness, precedence and succession) between two textual segments, and adopted SVM classifier to learn the relative order of them. They repeatedly concatenated two textual segments into one segment based on the relative order until all sentences were arranged. Nie et al. (2006) adopted adjacency of sentence pairs to sort sentences. Sentence adjacency was calculated based on adjacency of features of each sentence pairs. Adjacency between two sentences represented how closely they should be putted together in a set of summarization sentences. Ji et al. (2008) extended the above adjacency model. They proposed a cluster-adjacency based method to map each sentence of source documents to a theme by using semi-supervised classification method. The adjacency of sentences pair was learned from source documents according to adjacency of clusters they belonged to.
Our method is different from above ones in two aspects. First, we try to find the indirect relations of summarization sentences according to source documents. Second, the source documents are topic-related and most summarization sentences or their similar ones appear in more than one source document. Meanwhile, there are more than two summarization sentences or their similar ones may co-occur in a source document. Therefore, we can infer the order of summarization sentences by using the indirect relation between summarization sentences.

Motivation
Different documents have different writing styles and backgrounds. Besides, sentences that used to describe a same topic may have different forms. Therefore, source documents maybe cannot provide the direct information for sentence ordering. However, they still provide some indirect information which can be used to infer the order of summarization sentences.
Each source document for multi-document summarization is not an information island. They may have some overlapped information. For example, there are three documents to report a same news event. The first document describes its background and cause. The second one describes the origin, process and result. The third one describes the effect and comment. Each document has its own key point, but they have some overlapped information. The first and the second one report the cause in both while the second and the third one report the effect in both.
Each summarization sentence is extracted from a source document even though it is created or rewritten manually, it also can link to one or more sentences in source documents. Therefore, each summarization sentence is also not isolated and there are many sentences surrounding them in the source document. We call these sentences as the context of the summarization sentence. Furthermore, the source documents are in same topic and most of summarization sentences are presented in more than one document. In a word, a summarization sentence may have some similar sentences in source documents.
Accordingly, we provide a method to infer the order of summarization sentences by using their context. Let us consider an example: there are two documents, d 1 and d 2 , sketched in Figure 1, where ss 1 and ss 2 are two summarization sentences or their similar ones in source documents, s1 i and s2 i are the context sentence of ss 1 and ss 2 respectively. If ss 2 is similar to s1 1 , we have reason to believe that ss 2 and ss 1 should be adjacent and ss 2 should be in front of ss 1 because s1 1 is front of ss 1 . The highly similarity between ss 2 and s1 1 , the highly probability that ss 2 is in front of ss 1 .
If ss 2 is not similar to s1 1 , but it is similar to s1 2 , we also consider ss 2 should be in front of ss 1 , but the probability will higher because s1 2 is closer to ss 1 than s1 1 . Similarly, if ss 2 is similar to s1 3 , ss 1 should be in front of ss 2 .

Context Inference-based Sentence Orde ring Algorithm
From the analysis in section 3, we can infer the order of sentence by using the similarity between summarization sentences and their context. We compute the adjacent credibility of two sentences and then use a directed graph with weight to represent the order of summarization sentences. A vertex denotes a sentence. If two sentences are adjacent then an edge exists of them. The weight of edge is the adjacency credibility of two sentences. Figure 2 shows the order relation among summarization sentences, there is an edge from s 1 to s 3 and the weight is 0.5, which means s 1 and s 3 are adjacent, and the credibility of them is 0.5. From figure 2, we can find a path contained all vertexes, which has the maximum weight. The order of the path is considered as the best logical order of summarization sentences. Due to finding an optimal path in a graph is a typical NP problem; we will use an algorithm to find an approximate optimal path. We assume that the document set D= {d 1 , d 2 ... d n }, a document d j = {s j,1 , s j,2 , …, s j,m } where s j,i is the ith sentence in d j , the summarization sentences set SS= {ss 1 , ss 2 , ..., ss k }, the Graph G=NULL. For a sentence ss i in d j , its context sentence set is {d j -ss i }. The algorithm is described as follows. Input: D, SS and G Output: P, an ordered list that contains all summarization sentences.

BEGIN
Step 1: For each ss i in SS, add a vertex to graph G; Step 2: For each ss i in d j , compute the similarity sim(ss k , s j,l ) between each ss k ( (1) where rank(ss i ) is the sequence of ss i in the document it belongs to (e.g. the rank of second sentence of document is 2), w is the contribution of sentence similarity to the credibility, sim(ss k , s j,l ) is the similarity between ss k and s j,l . If there is no edge from ss k to ss i in G, then add an edge to G; else if weight(ss i , ss k ) is greater than the weight of existing edge, then update the edge.

} {
If ss i is in front of s j,l , the credibility weight(ss i , ss k ) is computed as follows: (4) where sim(w, s i ) is the maximum semantic similarity between word w and sentence s i , length (s i ) is the length of sentence s i .
Step 3: We use a feature-based method to find the first sentence, assume that there is a null sentence at the beginning of each source document and it contains a null feature (Nie, 2006). The probability of a sentence ss i is the first one is defined as follow: where K is the number of features in sentence s i . freq() is the frequency function, freq(feature t ) denotes the frequency of feature t in the source documents, freq(feature t , nullfeature) denotes the frequency of feature i and the null feature cooccurring in the source documents within a limited range (one or several sentences, we assign 3 to the range). Select the sentence with the maximum probability as the first sentence. Add the first sentence to P.
Step 4: Get the trail sentence of P, select the next sentence and add it to P. Given an already ordered sentence serial P: ss 1 , ss 2 … ss i which is a subset of the summarization sentences set SS. The next sentence can be found by formula (6) Step 5: If P contains all summarization sentences then exit; otherwise go to step 4. END

Test Set and Evaluation Metrics
Due to current methods provided their experiment results using DUC04 corpus, we also use it to conduct our experiment. DUC04 provide 50 source document sets and four manual summaries for each document set in its Task2. Each document set consists of 10 documents. In the DUC04, sentences in summaries are not directly come from the source documents and they are created by manually. Therefore, we need map them back to the sentences in source documents. For each manual summarization sentence, we find a sentence in source document sets that has the maximal similarity with it as its source sentence. These source sentences of each summarization are taken as inputs to ordering model, but sequential information is neglected. The output ordering of various models are compared with the specified ones in manual summaries. A number of metrics can be used to evaluate the difference between two orderings. In this paper, we adopt Kendall's τ (Lebanon, 2002), which was defined as: where N is the number of objects to be ordered (i.e., sentences), number_of_inversions is the minimal number of interchanges of adjacent objects to transfer an ordering into another. Intuitively, τ can be considered as how easily an ordering can be transferred to another. The value of τ varies from -1 to 1, where 1 denotes the best situation where two orderings are the same, and -1 denotes the worst situation where two orderings are completely reversed. Given a standard ordering, randomly produced orderings of the same objects would get an average τ of 0. For example, Table 1 lists three numbers of sequences, their natural sequences and the corresponding τ values.

Experimental Results
In order to get the best performance, we adjusted the parameters of our approach (θ and w). We adopted DUC02 as the development set to adjust parameters θ and w. In DUC02, it provide 60 document sets of approximately 10 documents each and one manual summarization of about 100 words for each document set in Task2. Figure 3 show the experiment results, from it, we can finally get the optimal value of θ and w (0.3 and 0.6 respectively). We assume Rd, Mo, Pr, Fa and Ca to denote random ordering, majority ordering, probabilistic model, feature-adjacency based model and cluster-adjacency based model which have been mentioned in section 2 respectively. Due to other methods didn't report their results on DUC04 corpus; we do not compare our method with them in this paper. We define our Context-based approach as Co. Table 2 shows the results of different methods. The τ value of our approach reaches 0.43, which outperforms other approaches. Also, the performance of WordNetbased similarity measure is slightly better than the TF-IDF based one. The reason may be that some authors will express the same meaning by using some different but synonymous words.   Table 3. Comparison of correct sentence inference From table 3, our method has the highest correction ratio of sentence inference, which mean that our method's strategy to choose next sentence is reasonable than that of others. Probabilistic ordering, a statistical method, tries to find the ordering clue from the corpus, but they ignore the importance of source documents. Basically, we know that people can easily find the order of summarization sentences according to the source documents. But it's difficult to give the order for the sentence base on a corpus without the source documents even for a knowledgeable man. Therefore, we believe the source documents can give us some effective and efficient information. Fa and Ca models sort sentences by using sentence adjacency, which is calculated based on adjacency of feature pairs. However, how to choose effective features is a potential issue of this kind of method. Besides, it doesn't always mean two sentences are likely to be adjacent when some words of them are adjacent. Our method is different from other ones in that we find the indirect relations between summarization sentences based on the context and make full use of the information which provided by source documents. Table 4 shows a further comparison among all methods. "Positive ordering" means that the result of ordering is better than that of random ordering and "Negative ordering" denotes the output ordering which gets a negative τ. From it, we can review that our method generates the most positive orderings while with the least negative orderings. Precision of first sentence of our method is less than that of Ca. If we assume the first sentence of summarization is known in advance, experiments show that the average τ value of our method could reach 0.562.

Models
Precision of 1st sentence  Table 4. Correction ratio of 1st sentence ranking

Conclusion and Future Work
This paper presents a novel method for sentence ordering in multi-document summarization application. The proposed method first computes the similarity between summarization sentences and context of other summarization sentences, judge whether or not two sentences should be adjacent, and then compute the reliability according to the similarity. The experimental results review that our method outperforms other sentence ordering methods on DUC04 corpora. For further work, we will change the strategy to improve the accuracy of first summarization sentence; therefore, the τ value could have an obvious improvement. In addition, we will conduct more experiments to verify the effectiveness and efficient of our method using manual evaluation.


Determination of Spectral Parameters of Speech Signal by Goertzel Algorithm

The speech is a sound different from all other sounds, and information transmission by speech is a basic mechanism of human communication. The study of speech as one of the main factors of human communication is a multidisciplinary problem thus different fields of science deal with particular aspects of this phenomenon. At the beginning of 21st century the biggest scientific challenge in the segment of speech technologies is the realization of spontaneous communication between human and computer. Technology’s solution of recognition and synthesis of speech can’t compare with human perceptions and production of the speech. That means our nature, God given, emotion and acts aren’t still enough explored, and we have here a lot of space for learning, watching and scientific exploring. With talking we can transmit to our surroundings complicated information expressed by linguistic contents, but that aren’t the least level communication’s possibility of speech sound. Sound, which transfers speech signal, carries many information which speech signal contains and decodes that information as well as their production is spontaneous and very simple for human perception (Tomas, 2006). Furthermore, identification, evaluation and selection of certain information contents from a total audio picture are at a consciousness level. Besides linguistic information, the speech signal contains a variety of non-linguistic information from which a listener can get a great deal of information that is not contained in the linguistic information, such as: gender, speaker's age, intentions, a psychological state, and situation the speaker is in; an emotional state of the speaker; surrounding the speaker is in, etc (Tomas et al., 2007a; 2007b; 2009). Emotions are definitely one of the most important non-linguistic speech attributes (Dellaert & Waibel, 1996). Speaker’s emotional state has influence on the articulation and phonation pronounced phonemes. A vocal fold vibration and articulators movement depends on emotional state of speaker. Each speaker’s emotion forms vocal chords and vocal system that is acoustically shown through variations of the speech signal parameters. The main problem is to define the parameters that create certain illusions in a speech signal, namely to define correlations between emotions and measurable variations of speech signal parameters (Amir & Ron, 1998; Ramamohan & Dandapt, 2006; Tao et al., 2006). In last few years, numerous investigations have been done in order to determine correlation of the speech parameters and the speaker’s emotional state (Amir, 2001; Amir & Ron, 1998; Cowie

. In this chapter, the spectral parameters during the pronouncing of vowels /a/ and /e/ in Croatian (Serbian, Bosnian) language in different emotional conditions has been analyzed. This is done by analyzing structure of vowels /a/ and /e/ spectral parameters for three emotional states: neutral, anger and speech under stress. Impact of emotions on speech signals parameters was analyzed: formant frequencies structure, pitch frequency, dynamics of pitch parameters and pitch harmonics structure. This chapter also describes the Goertzel algorithm and its implementation with speech signal spectral analysis. It describes the method of formant analysis of vowels as well as the vowel harmonics analysis of Goertzel algorithm. The Goertzel algorithm provides fast and simple determination of speech signal structure and precisely determines speech signal frequency values independently of speaker. In analysis of certain speech signal parameters, non-linguistic speech signal attributes can be recognized. The purpose of introducing Goertzel algorithm into the frequency analysis of the digital speech signal is a fast signal processing, and determining of spectral energy from the digital signal in desired frequency bins on the basis of pre-defined coefficient m. The chapter is organized as follows. In Section 2 the phonetic characteristics of the Croatian language are shortly presented. In Section 3 recording of speech sound materials as well as Goertzel algorithm are shortly presented. Spectral energy analysis of isolated vowels and formant vowel structure tracking by Goertzel algorithm is presented in Section 4. In Section 5, the glottal speech during the pronouncing vowel /a/ has been analyzed. The correlation of certain pitch harmonics parameters and speaker's emotional states were investigated. In Section 6, an imaginary idealized futurist communication model of speech transmission is presented. Section 7 concludes the chapter with final remarks.

The basic phonetic properties of the Croatian language
The knowledge of linguistics and phonetics is of great importance in a large number of applications of digital speech processing, such as synthesis and speech recognition Flanagan, 1972;Ipsic & Martincic-Ipsic, 2010. The study of linguistics deals with language rules and their impact on human communication, while phonetics deals with the study and classification of sounds in speech. Language is the basic human communication and it can be transferred by speech or writingreading. The smallest segment of speech sound is the phoneme. The voice (phoneme) in written language is a letter (grapheme). The voice is the smallest spoken unit that can be isolated from a word of some language, i.e. the voice represents the smallest noticeable discrete segment of sound in continuous speech flow. It is defined as articulated sound in speech which represents the material realization of an abstract linguistic unit -phoneme. The phonemes are language-specific units and thus each language needs a declaration of its own phonetic alphabet (Sigmund, 2009). The number of phonemes commonly in use in each literary language varies between 30 and 50. The number of phonemes and graphemes (letter) in Croatian language is the same (30) and they are in alphabetic order : a, b, c, č, ć, d, dž, đ, e, f, g, h, i, j, k, l, lj, m, n, nj, o, p, r, s, š, t, u, v, z, ž. In Croatian as well as kindred South Slavic language (Serbian, Bosnian) any word can be graphically represented using 30 letters (grapheme). Acoustic realization i.e. pronunciation of graphemes is very simple in Croatian language. Each written symbol (letter i.e. grapheme) has its own phoneme and vice versa. This rule stands for all words in Croatian language. Therefore reading and writing in Croatian language is very simple. Based on the configuration and the opening of the vocal tract, Croatian sounds are divided into three basic groups. These are: -vowels ('open' sounds): a, e, i, o, u semi vowels ( sonants) : j, l, lj, m, n, nj, r, v consonants: b, c, č, ć, d, dž, đ, f, g, h, k, p, s, š, t, z, ž Semi vowels and consonants when used less specifically are put into the same group, i.e., the consonants. There are several classifications of the sounds in Croatian language. There are voiced speech sounds and unvoiced speech sounds. Voiced speech sounds classification is the most important in speech technologies. In speech, pitch is not present in all sounds (Ahmadi & McLoughlin, 2010). Pitch harmonics are of a very high intensity in vowels, less high intensity in semi vowels and the least intensity in voiced consonants (b, d, dž, đ, g, z, ž). The rest of the consonants (c, č, ć, f, h, k, p, s, š, t) are unvoiced speech sounds and they have no pitch. In spoken language the vowels are sound that carry the most information. Vowels constitute the most important speech sounds group and are characterized by the fact that these are the sounds of the greatest energy. When the vowels are produced the majority of the vocal tract is open and in the course of the entire duration of pronunciation the vocal cords vibrate. The primary purpose of vowels is to connect the consonants into the syllables, i.e., the formation of utterable words. In written language, the records of vowels often carry little information, i.e., in most cases the recognition of text messages is possible even when they are completely eliminated out of words.

The remaining phonetic properties of the Croatian language
Croatian orthographic rules are based on the phonological-morphological principle which enables automatisation of phonetic transcription. Standard definition of orthographic to phonetic rules, one grapheme to one phonetic symbol (Ipsic & Martincic-Ipsic, 2010). Although there are only 30 different sounds in the language, a far greater number of modifications of the same appear in a real speech. The manner of articulation of each sound depends significantly on its context, i.e., the sounds on its left and right side. This phenomenon is called coarticulation. Therefore, high-quality synthetic speech cannot be obtained by simply merging the 30 discreet sounds. Also it is important to emphasize that the transitions from one sound to another are not sudden (step), but are very gradual and are defined by the gradual transition of the articulator from the initial position corresponding the first sound towards the new position corresponding to the next sound. In this process, the vocal tract passes a series of inter-states, which causes the formation of a series of transitive sounds of relatively short duration. The elimination of these transitions significantly disrupts the naturalness of a synthetic speech. For the purpose of solving this problem, and with the simpler speech synthesizers, the removed pairs of phonemes or so called diphthongs recorded from the actual speech are used as the basic elements of synthesis. In this way, among the basic elements, there are present and also the transitions mentioned above.

The segmenting of sounds in a continuous natural speech
The uttering of vowels takes from 50 to 300 ms. Consonants are only the processes of invibrating and out-vibrating of the previous and the following vowels, with a duration of 2 to www.intechopen.com 40 ms. The shortest speech sounds last only as it's necessary for the ear to recognize the tonal pitch. The sounds in speech and music should be long enough for the ear to analyze them tonally, about at the same time they must not follow each other too fast so as to prevent masking of the next by the previous masked. For the research of the variations of the duration of sounds in speech segments during the expression of speech emotions, it is indispensable to analyze the uttered segments on the words as the units of the linguistic context as well as segment sounds from the isolated uttered words. Since there have been noted problems of dichotomy between spoken words and words as units of context in the speech continuum, together with the problems of phonetic positions, the communication situation, individual variations and the like, it is important for the needs of analysis of the manifestations of speech expressions of emotions, though preliminary, to establish criteria for the segmentation of sounds in a continuous speech.

Speech material
Twenty students were chosen for this research (all male). The acoustic recordings were made in speech recording and processing Croatian Telecom Mostar studio on mixing board (16channel MIC/LINE mixer Mackie 1604-VLZ PRO). Each student was asked to pronounce five speech phrases. They are four words: "mama", "ma", "je", "ne" and loudly pronounced vowel "a". A Croatian word "mama" consists of two equal syllables "ma" and it means mother i.e. mum. Word "ma" is a mono syllable word and it is often used to express anger. Word "je" means OK and word "ne" means NO. A certain emotional state was simulated for each speech phrase. The word "mama" was pronounced in a neutrally emotional mode with a bit of sadness, while the word "ma" was pronounced simulating anger and surprise. Also, the pronunciation of the word "je" was without emotions i.e. neutral voice while in pronouncing the word "ne" anger was simulated. Loudness as the expression of stress was simulated by loudly pronounced vowel /a/ in the ˝Jako A˝ speech file. Recording of vowel /a/ in the ˝Jako A˝ speech file was realized in different conditions due to dynamic of the loudly pronounced vowel (less sensitive microphone). The stress conditions considered in this study include simulated anger and loudness (Bou-Ghazale & Hansen, 2000). The best speaker was chosen by audio testing. Each speech phrase of all 20 speakers (students) was assessed on a scale from 1 to 5. Points were determined for each speaker by taking the sum of the marks for each of his/her five speech phrases. The speaker with the highest score needed to record given speech material another four times. (This consisted of 20 files, 5 for each speech phrase) Finally the best recording of each speech phrase was chosen for a sample. Speech files ˝Jako A˝, ˝MAMA˝, ˝MA˝, "JE" and "NE", were being recorded in Sound Recorder program with sampling frequency of f u =8000 Hz and 16 bits of mono-configuration and resolution of quantization. Further, the vowel /a/ was isolated from the MAMA and MA files. That is the first /a/ in the word MAMA. In the same way vowel /e/ was isolated from the files JE and NE.

Spectral analysis
Each temporal signal carries certain frequency contents. The presentation of temporal signals in the frequency domain is very significant. Frequency representation of a signal mostly enables better analysis of appearance which the signal represents. The speech is www.intechopen.com signal with time-dependent spectral content. Time-frequency representations are often used for the analysis of speech signals due to their non-stationary nature. For a practical application the speech signal can be processed in various ways, other than time-domain, to extract useful information. A classical tool is the Fourier transform (FT) which offers perfect spectral resolution of a signal (Shafi et al., 2009). Fourier techniques have been a popular analytical tool in the study of physics and engineering for more than two centuries and it is the prime method used to transfer a temporal signal into frequency domain. With the arrival of digital computers, it became theoretically possible to calculate the Fourier series and Fourier transform of a function numerically (Dutt, 1991). A major break-through in overcoming this difficulty was the development of the Fast Fourier Transform (FFT) algorithm in the 1960s which established Fourier analysis as a useful and practical numerical tool. The FFT converts a time-domain sequence x(n) into an equivalent sequence X(k) in the frequency-domain. Spectral analysis of the speech signal in most of published studies was obtained by applying a Fast Fourier Transform (FFT). In this study Goertzel algorithm is used in speech signal spectral analysis.

The application of Goertzel algorithm at the analysis of speech signal
Goertzel algorithm is often applied technique at the realization of digital DTMF (Dual -Tone Multiple Frequency) receivers. DTMF signalling, the so-called tone dialling, is done running audio tones that transmitter generates and that needs to be decoded on the receiver's side for the purpose of the further processing. DTMF transmitter (encoder) generates a compound audio signal formed of two mutually harmonically independent frequencies by combining of eight given frequencies. Receiver needs to decode the frequency contents of the received audio signal. DFT plays an important role in the implementation of algorithms into the systems for digital signal processing. The usage of the FFT algorithm significantly reduces the computation time of Fourier transformation. If you need to detect one or more tones in the audio signal or only one or a few frequencies, there is a lot faster method. The Goertzel's algorithm allows decoding of a tone (frequency) with much less processor load compared to the Fast Fourier Transform (FFT). Since the speech is also a complex audio signal, we draw the same conclusions, which means that at the processing of digital voice signal, we can apply the same technologies and algorithms that we will, of course, adapt to the needs of the analysis of speech signal. It is evident that for the analysis of digital voice signal Goertzel's algorithm can be used. The main advantage of this algorithm is that the coefficients in the equation for a particular frequency are fixed, what makes the calculation much simpler. At the analysis of speech we use the algorithm that at the exit, instead of decoding of levels at the desired frequencies, gives the spectral energy of the analyzed frequency band, i.e. Goertzel's algorithm is used for filtering. When determining spectral energy of digital signal it is necessary to remember only two previously calculated values, from the last step N, difference equations of Goertzel's algorithm, which we calculate by the 'step by step' method, while for the detection is required to determine the signal spectrum at the end of filtering. The purpose of introducing Goertzel algorithm into the frequency analysis of the digital voice signal is a fast signal processing, and determining of spectral energy from the digital signal in desired frequency bins on the basis of pre-defined coefficients m. Then, by the analysis of the calculated energies, for particular bins, we can get a lot of information contained in the speech signal. Also, since using the Goertzel's algorithm we can determine the parameters required for the recognition of speech, the speaker, emotions and other nonlinguistic attributes, it is logical that Goertzel's algorithm can be implemented for the realization of these activities.

Basic Goertzel algorithm
The basic Goertzel transform was derived from the discrete Fourier transform (DFT). Algorithm was introduced by Gerald Goertzel  in 1958 (Goertzel, 1958). It's an extremely efficient method of detecting a single frequency component in a block of input data. Figure 1 depicts the signal flow for the basic Goertzel algorithm as each sample is processed (Kiser, 2005). The signal flow of the algorithm produces an output y 0 for each sample processed. The output is a combination of the current ADC sample added to the product of the previous output y 1 multiplied by a constant minus the previous output y 2 . Figure 1 may be written as: y 0 is the current processed output, x 0 is the current ADC sample, y 1 is the previously output, and y 2 is the next previously processed output, m is the frequency domain bin number. N is the sample block size. Input samples are processed on a sample-by-sample basic. Processing continues over a block of input data length N. The Goertzel transform is normally executed at a fixed sample rate and a fixed value of N. To detect multiple frequencies (i.e. frequency bins), each frequency must be assigned its own coefficient and then the value is used in equation (1). Depending on the number of desired detection frequencies, there will be an equal number of equations like equation (1) that must be executed once during each sample. At the end of a block of data, the spectral energy of each frequency bin will be computed and validated.

Spectral energy of Goertzel bins
The Goertzel algorithm is a filter bank implementation that directly calculates one Discrete Fourier transform (DFT) coefficient. The Goertzel algorithm is a second-order filter that extracts the energy present at a specific frequency (Bagchi & Mitra, 1995;Felder et al., 1998). Therefore, for the analysis of digital speech signal a system that will give a spectral energy of the analyzed (filtered) signal bandwidth (bin) is suitable. Such system is shown in Figure 2.
After a block of data has been processed, spectral energy for the signal of interest (frequency bin) is determined by y 1 and y 2 variables. The sum of the squares of y 1 and y 2 are computed to determine the spectral energy of a particular frequency bin. One of the advantages of the Goertzel transform is that spectral energy computation needs to be performed only at the end of a block of data. After a block of data has passed through equation (1) (filter part), the spectral energy of Goertzel bins (frequency bin) is simply determined by equation (2) (energy part). The Goertzel algorithm implementation has the tremendous advantage that it can process the input data as it arrives. The output value is only needed for the last sample in the block. The FFT has to wait until the entire sample block has arrived. Therefore, the Goertzel algorithm reduces the data memory required significantly.
In the analyses, spectral energy distribution of isolated vowel /a/ is in the final invariant time position during its pronunciation. Our software built for these researches allows selecting of sample, from the analyzed file, from which it will start loading samples in the equation (1). The analyses (all five speech files of chosen speaker) begin from sample k=50 which mean that the first 6,25 ms of speech file are skipped. Thereby, beginning transition of vowel pronunciation was avoided. The speech file is first loaded into the software, after that frequencies tuning coefficientsm are loaded, followed by sensing start samplek and number of samples -N that is number of Goertzel transform iteration i.e. the equation (1).

Formant vowel structure tracking by Goertzel algorithm
In many science papers from this area, for the most significant parameters of speech signal which fertilized change under emotions and stress there has been defined basic frequency and energetic structure of pronounced words. Also, and analysis of formants show that exist considerable departure in formants structure at pronounced speech in different emotion's state or under impact of stress. Spectral energy analysis of isolated vowels by applying Goertzel's algorithm gives a proper illustration that enables classification of emotions by comparing the energy, and their formants. Formant frequencies structure gives a good view to determine linguistic as well as nonlinguistic speech meaning and it represents a significant parameter within the synthesis and speech recognition procedure. In fact formants are groups of harmonics enhanced by the vocal tract resonance. There is a resonance i.e. the enhancement of spectral energy in speech signal spectrum at formant frequencies (Tomas & Obad, 2009). The difference between formant frequency structures of vowel /a/ is very informative about the speaker's emotional state. The dominant influence of emotions is on the first formant. Frequencies of the first formant are increased in expression of the emotions of anger and happiness but decreased in case of fear and sadness. Therefore, changes of vowel /a/ formant structure are not sufficient for separation of primary emotions: anger, happiness, fear and sadness.

The determination of the formant vowels structure using the Goertzel algorithm
We will analyze the speech signal, vowel / a /, recorded with the "Sound Recorder" software with the sampling frequency fu = 8000Hz, and stored under the title "Jako A". If the Goertzel's transformation, equation (1), is performed on the time segment of the recorded speech signal of 25ms duration, we have N = 200 samples of speech signal, which we process by the algorithms described by the equation (1) and the equation (2). From f u = 8000Hz and N = 200 result's the frequency band for each Goertzel's bin: B = f u / N = 40 Hz.
The first formant will be sought in the frequency band (400 -720) Hz. This frequency band is divided in the next 8 bins. The frequency bands of the eight bins, as well as their central frequencies, on which we base the calculation of the coefficients of tuning the frequency domain m, are shown in Table 1.  Table 1. The division of frequency band into bins Now we will calculate the coefficients of tuning the frequency domain m for each bin, which will be included in the expression 2cos(2πm/N) and the value of that expression, that is a constant -different for each bin, will be stored on particular memory locations, so that the processor spends less time for the calculation of the equation (1), since the equation is executed in 200 steps. We will illustrate this for bin 1: By the inclusion of the obtained values m k in the expression of 2cos(2πm k /N), we get for each bin its coefficient k m(k) for the calculation of the equation (1). The outline of all values is given in Table 2 (Tomas & Obad, 2008). Now we have all the necessary input constants for the calculation of equations (1) and (2). The samples of speech signals are entered in the same order that they are sampled in the equation (1) (2). For each bin we calculate the spectral energy by entering the coefficients of the bin into the equation (1). In fact, in this way we determine the spectral energy on the frequency of f bin in its corresponding Goertzel's bin.  Table 2. Display of parameters, coefficients and spectral energy of bins The same procedure is for other formants, which would be searched for in higher frequency bands. Also, we do not have to take bins in a row. Also, by the simple choice of constants we calculate the bins that overlap. In addition, if we need greater selectivity of bins we are able to accomplish that by increasing the number of input samples N with unchanged sampling frequency, or vice versa. From Table 2 it can be seen that the first formant of the vocal /a/ is positioned in the bin 7 in the frequency band (640-680) Hz. Also, based on the value of energies in bins 6 and 8 we read that the formant is positioned at the beginning of the bin 7, i.e. that it is in the frequency band (640-660) Hz. However, for the majority of analysis it is not necessary to determine the exact frequency positions of formants but the division of the bins is quite enough. The conclusions about the emotional states of speakers, as well as the recognition of speech and speaker can be drawn on the basis of relations between the energies of selected bins. For more precise determination of the frequency of the first formant we can carry out the following procedure of Goertzel's transformation on bins 6 and 7. To do that we would www.intechopen.com increase the number of samples of speech signal (e.g. N=400) for that frequency band so that the frequency band for new Goertzel's bin would be: B= f u /N=8000/400 =20 Hz. The analyzed frequency band (620-680) Hz can be divided into three new bins and for these three new bins we can calculate spectral energies. However, with N = 400, i.e. 50ms of speech signal we would come out of the frame of quasistationariness. On the other hand we could with N = 200 by selecting m-coefficient of tuning of a segment of frequency domain, analyse the bands with overlapping of neighbouring bins, e.g. 50% and get the spectral energy on the analysed frequency band with the distance of 20 Hz instead of 40 Hz as in Table 2 where the bins are in a row with no overlapping.

Influence of emotions to the formant structure of vowel /a/
Using the results of the (Vojnović, 2004) for Serbian language with the PRAAT program for male speakers, we shall make an analysis applying the Goertzel algorithm. Achieved results of the influence of emotional state of males to the formant structure of vowel /a/ are displayed in the Table 3. A similar analysis was done for German language (Kienast & Sendlmeier, 2000). Hence, with male speakers, no matter the emotional state of a speaker, while pronouncing vowel /a/ formants should be looked for in the following frequency bandwidths: -Formant 1 in the bandwidth of (400-700) Hz -Formant 2 in the bandwidth of (1350-1500) Hz -Formant 3 in the bandwidth of (2500-2700) Hz  Table 3. Average middle frequency of the first three formants of male speakers for different emotional states Figure 3 illustrate the comparison of spectral energies of the vowel /a/ within expected ranges of the first three formants (the red line is isolated vowel /a/ from "MA" speech file, and the blue line is isolated vowel /a/ from "MAMA" speech file). In comparison of energy values for emotions classifications, in equations (1) and (2) we adjust the bins of wider frequency bandwidth. At the sampling frequency of 8 kHz it is convenient to use N=100 or N=200 and so get the bins in bandwidth of 80Hz and 40 Hz respectively.  (c) Third formant Fig. 3. Bandwidth of the first three formants "A" N=200, f=8 kHz, overlapping of bins 50%

Influence of emotions to the frequency composition of vowels /a/ and /e/
pronouncing /a/ from MA in anger, positions of formants frequency are higher than while pronouncing it without emotions in articulation of word MAMA, which affirms the researches illustrated by the Table 3. Also, it is obvious that the energy of spectrum /a/ from MA, of articulation in anger, is higher that the energy of the neutral articulation of vowel /a/ from MAMA within all three frequency bandwidths observed. Also, as we positioned the first formant for "JakoA" in the bandwidth of 640-660 Hz, we may conclude that "JakoA" has been articulated in the emotional state that might be anger, happiness or some stressful emotion, since F1> 600 Hz (Tomas & Obad, 2009). On the following figures the blue line is neutral articulation (vowel /a/ is isolated from "MAMA" speech file and vowel /e/ is isolated from "JE" speech file), the red line is www.intechopen.com articulation that expresses anger (vowel /a/ is isolated from "MA" speech file and vowel /e/ is isolated from "NE" speech file). in case of vowel /e/ the formants frequencies for different emotions are more distanced than it is the case for /a/ , which is shown by Figure 4 and Figure 5. For comparison of spectral energies and formants in classification of emissions we may use the bins of wider frequency bandwidth. For the case of sampling frequency of 8kHz presented bandwidths of 40 Hz and 80Hz defined by the numbers of samples N=200 and N=100 respectively give a proper graphical overview and good results. Advantage of using bins of wider bandwidth is that we cover the analyzed bandwidth with small number of bins that program may process in one cycle since it uses the same input samples. Of course each bin has different coefficients and final values of spectral energy. It is obvious from the Figure 4 and Figure 5 that the spectral energy of isolated vowel /a/ from MA in articulation that expressed the anger is much higher compared to /a/ from MAMA in neutral articulation that expresses no emotions. It is noticeable as well that in the ranges of lower frequencies that difference of energy is less expressed, while in the range of middle frequencies in the bandwidth of 1-4 kHz the energy difference is expressed the most. It is illustrated by Figure 6, where there are precisely calculated spectral energies in that frequency bandwidth with distance points of 12,5 Hz (N=320, f= 8 kHz, overlapping of bins 50%). Figure 6 (a) illustrates the comparison of spectral energies of isolated vowels /a/ digitalized with sampling frequency of 8 kHz, while Goertzel's transformation was applied with 320 samples of digitalized signal so that the bins were of 25 Hz bandwidths. Except this, the neighbouring bins are overlapping 50% so the spectral energy was calculated for the frequency bandwidth of 1-2 kHz with calculating distance points of 12,5 Hz. Figure 6 (b) illustrates the comparison of spectral energies of isolated vowel /a/ in the bandwidth of 2-3 kHz. Hence, if we perform the identification and classification of emotions with only energy comparisons of isolated vowels /a/, we may do a quick analysis by selecting several bins in string from the frequency bandwidth of middle frequencies (1-4 kHz) and to compare the sums of the energies of those bins as it was explained earlier (Tomas & Obad, 2009

Pitch harmonic parameters
Spectral energy analysis of isolated vowels by applying Goertzel's algorithm enables classification of emotions by comparing their harmonics (Tomas et al., 2007b). For selected number of samples N=320 duration of time frame is 40 ms, which still satisfies quasistationary speech conditions. Harmonics parameters of vowel /a/ isolated from ˝Jako A˝, ˝MAMA˝ and ˝MA˝ speech files are shown in Table 4. Recording of vowel /a/ in the ˝Jako A˝ speech file, was realized in different conditions (less sensitive microphone), therefore absolute and decibel measurements values are not identical to values of vowel /a/ in ˝MAMA˝ and ˝MA˝ speech files. Thus, their dynamic and histogram bands are not comparable. However, these changes do not influence frequency and harmonic structure.
The following Figures illustrate vowel /a/ harmonics. There is dB and absolute amount of spectral energy in Figure 7 and Figure 8. Figure 7 shows frequency band 0-600 Hz, N=320 and frequency bin overlap of 50 %. Sampling frequency is 8 kHz, thus bin width is 25 Hz, and spectral energy results are shown by 12,5 Hz points distance. On Figure 7 the blue line is neutral articulation (vowel /a/ is isolated from "MAMA" speech file), the red line is articulation that expresses anger (vowel /a/ is isolated from "MA" speech file).  Table 4, the lowest basic frequency has vowel /a/ isolated from the MAMA file, spoken in a neutral emotional condition, while the highest basic frequency has vowel /a/ isolated from the MA file, spoken with angry and surprise simulation. Amplitudes dynamic in angry is higher than in neutral emotional speech. Also, amplitudes dynamic of ˝Jako A˝ file would have been higher too if it had been recorded in the same conditions. Harmonics structure in all three emotional conditions is different indicating emotional influence, but for determination of correlations more analyses are required. It is necessary to import parameters -indexes that will clearly describe harmonics structure. The Goertzel algorithm provides fast and simple determination of speech signal structure and precisely determination of speech signal frequency values independently of speaker. In analyzes of certain speech signal parameters, non-linguistic speech signal attributes can be recognized (Tomas et al., 2007b).

Harmonics structure normed indexes (HSNI)
Implementation of parameters provides fast, efficient and clear overview of analyzed speech files harmonics structure. HSNI are parameters that are fast and simply calculated and they provide articulate and fast classification of emotions (Tomas et al., 2007a). Table 4 shows structure of the first five harmonics. Numbers, that defined intercomparison of the absolute mounts of energies, are assigned to harmonics. Energies are identified by Goertzel algorithm with 12,5 Hz distance frequency, at the harmonic frequency. If energies are identified with higher calculating precision of 2,5 Hz, for our speech files amounts are shown in the Tables 5, 6 and 7. In the Tables there are exact amount of frequencies at which spectral energies amount are maximal. It is evident that harmonics structure in Tables 5. and 7. are changed after more precise calculations with harmonics structure in the Table 4. There are permutations of the harmonics H2 and H3 in the Table 3 and H4 and H5 in the Table 7 respectively.  "Jako A"Index I k 0,581 0,0743 0,093 0,363 1 Table 8. The first five HSNI of the vowel /a/ Absolute amounts of spectral energies are normed thus spectral energies amounts of every harmonics divide with maximal amount harmonic spectral energy. That defines parameters shown in the last rows of Tables 5., 6. and 7 . These parameters are called HSNI and they are easily defined by equation (5):

Harmonics
I k is index of the H k harmonic, E(H k ) is spectral energy amount of the H k harmonic and E(H max ) is spectral energy amount with the highest absolute amount. In the Tables 5, 6 and 7 calculated amount of I k index are shown in the last rows and amount of E(H k ) are shown in the next to last rows of the Tables. These indexes are parameters for recognition and classification of emotions and others non-linguistic speech attributes (Tomas et al., 2007a).  Table 8. Also, graph of indexes linear interpolation is shown in Figure 10. The graphs confirmed that implementation and analyze of these indexes provide recognition and classification and others non-linguistic speech attributes.

The area of stress
The lines on figures 9 and 10 show graphs of HSNI of vowel /a/ for our chosen speaker. The graph (lines) of indexes linear interpolation of vowel /a/ is different depending on the speaker's emotional state. One can assume that the graphs of other vowels will behave www.intechopen.com similarly. That means that the curves of all five vowels, articulated by one speaker, for a certain emotion will almost overlap. Therefore, we will have areas of emotions. The area of stress and the expected areas of articulation without emotions (without stress) for our speaker are shown on the following figure.
- If we track a speaker's HSNI graph during continuous speech, (without emotions and stress), the graph will be in the blue area. However, if for any reason the speaker experiences stress (ex. A pilot experiencing stress because of an unexpected situation on flight) the HSNI graph will no longer be in the blue area but rather in the red area which is the area of stress. In this chapter, HSNI, the lines of emotions, and the area of stress are presented on the basis of the first five harmonics. To get better results, it would be desirable to work with a larger number of harmonics. In order to detect stress in our speaker, only the first and higher (4 and 5) harmonics indexes are relevant, while the second and third indexes stay in the same area whether the speaker experiences stress or not. Therefore, by tracking the shape of HSNI lines and the area in which the lines are located, stress of a speaker can be detected. When tracking the area of stress, instead of tracking the lines we can track HSNI points only. As far as our speaker is concerned we should track when the first, fourth and fifth HSNI point enters the red area. Expected areas with and without stress for our speaker are shown on Figure 12. In many applications the emotional state of one speaker (the pilot) is tracked. It is especially very important to determine when that speaker undergoes stress Zhou et al., 2001). It is shown how the harmonic frequencies change if the speaker changes emotional states. Besides that, during speech in one emotional state the basic speech frequency is not fixed. Therefore, the HSNI and the areas of emotions are practical for the classification of emotions and especially for stress detection. It is known that the frequencies of basic harmonics are different for male, female and child speakers. The main feature which can speaker's sex distinguish is fundamental frequency children is so different that they are often treated as "the third sex". Most values of F0 among people aged 20 to 70 years lie between 80-170 Hz for men, 150-260 Hz for women and 300-500 Hz for children (Sigmund, 2008). Therefore, in many applications in speech technologies these three groups of speakers must be programmed separately. HSNI can probably help to overcome this problem.

The voice communication channel model
Communication with computers, by means of speech has been a topic frequently regarded as a science fiction. In the last thirty years technology that has enabled speech recognition and its synthesis has made exceptional developments. At the same time, we are witnesses of explosion in systems used for recognition and synthesis of speech with an additional dimension: emotions. If standard voice communication channels are enhanced by compatible ASR and speech synthesisers, which code linguistic and non-linguistic speech parameters with common protocol we get communication model with negligibly short link engagement in comparison to duration of the actual conversation (Tomas et al., 2007a). Transfer of voice information content down the line would take short time, whereas voice through the microphone terminal on the side of speaker and voice through the synthesiser on the side of listener are in real time. Actually, at transfer we use coded indexes of non-linguistic attributes of the speaker's voice (speaker's voice characteristics) and coded indexes of linguistic information content from voice database. These indexes are coded data of linguistic and non-linguistic information with which are passed on the linguistic meaning of the conversation textual content and non-linguistic attributes of the speaker. This model would have been more practical and simpler in an enclosed system, where the number of participants in communication is limited as well as the pool of words and expressions which are used in communication. Naturally, improvement of systems for synthesis and recognition of voice will enable implementation of this model in open communication systems.
A statement is a pronunciation of one or more words that have a single meaning to the computer. A statement can be one word, several words, a sentence or several sentences. The representation of the described voice communication model is shown in Figures 13 and 14. The Figure 13 shows speaker's side of the model, whereas Figure 14 shows receiving end, i.e. the listener's side. Fig. 13 which transmits them. On the receive side non-linguistic information decoder recognize speaker and send information about non-linguistic parameters of speakers voice to a synthesizer. After non-linguistic information exchange, transmitter ASR recognizes spoken words and statements. They are coded in the speech base coder and are forwarded to receiver like coded indexes. On the receiver side, speech base decoder decodes received words and statements. A speech synthesizer produces them with recognized speaker voice. Of course, this basic model can be upgrade depending of communication requirements i.e. (military, police, etc.).

Model application possibilities
A telephone PSTN channel was used only as an illustration and packet networks would be suitable for speech transmission by this model. It would also be very convenient to divide the information part of the packet into two parts where linguistic speech information could be transmitted through one part and nonlinguistic information could be transmitted through the other, as shown in Figure 15. Due to nature of speech itself, it is not necessary for each packet to contain the nonlinguistic part, so it possible to get even a better efficiency of this model. This is very important with the realization of speech communication through networks with reduced speed and capacity channels such as VoIP that is the most current one today but also the other packet networks and their protocol. Application possibilities of this model are multiple and we will presume mention some of them: -Closed communication system -Open communication system -Speech to text -Conversation of different languages speakers if an interpreter is within the model -An aid for persons with special needs -VOiP -Speech message transformation systems -Automatic speech translation from Croatian intro other world-wide languages and via versa -E-mail and SMS messages reading -Text to speech -Etc. Finally, stress and emotions classification can also be employed in forensic speech analysis by law enforcement to assess the state of telephone callers or as an aid in suspect interviews.

Nonlinguistic Information
Header packet www.intechopen.com The majority of studies in the field of speaker stress and emotion analysis have concentrated on pitch, with several considering spectral features derived from a linear model of speech production.

Conclusion
Each speech message, besides linguistic information content (semantic meaning of spoken text), contains non-linguistic characteristics of speaker. The non-linguistic characteristics are correlated to certain acoustic characteristics of speech signal. Studying non-linguistic information's contents in speech signal is quality jump in speech analyses. The aim of many future researches will be finding suitable acoustic characteristics of speech signal that, besides the linguistic contents, will be used for a reliable determination of many nonlinguistic contents in speech signal. In this chapter the correlation of certain spectral parameters and speaker's emotional states are investigated. It has been reported that the spectral parameters vary with different emotions. A detailed study of all the spectral parameters can provide information on their dependence on different emotions. Defining and valuation of the parameters that are relevant for recognizing emotional speech attributes is the newest element of research in speech scientific discipline and also qualitative improvement in speech technologies. It is clear that we are at the beginning of the process of research of the one complex scientific field that will open a lot of new segments in many scientific disciplines.


A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface&Modified k-Means Clustering

Facial expressions convey non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of human face based on facial expression can be an important component of natural human-machine interface. It may also be used in behavioural science. Although human can recognize the face practically without any effort, but reliable face recognition by machine is a challenge. This paper presents a new approach for recognizing the face of a person considering the expressions of the same human face at different instances of time. This methodology is developed combining Eigenface method for feature extraction and modified k-Means clustering for identification of the human face. This method endowed the face recognition without using the conventional distance measure classifiers. Simulation results show that proposed face recognition using perception of k-Means clustering is useful for face images with different facial expressions.

INTRODUCTION
Over the past 20 years [1], considerable amount of works have been carried out for automatic recognition of facial expression by different researchers using parametric as well as non-parametric techniques. The work proposed by M.A.Turk & A.P.Pentland [2,3], in "Eigenfaces for Recognition" the input images is projected onto the face space using principle component analysis called Eigenfaces. The recognition is performed using various distance classifiers and a correct distance-threshold. Sang-Jean Lee et al. [4] developed a system that can locate and track person's head in a complex background and recognization is done using pre-extracted face images on a feature space using PCA. Moon H. et al. [5] designed a generic PCA algorithm for experimenting on FERET image set with varieties of issues such as illumination Facial expressions convey non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of human face based on facial expression can be an important component of natural humanmachine interface. It may also be used in behavioural science. Although human can recognise the face practically without any effort, but reliable face recognition by machine is a challenge. This paper presents a new approach for recognizing the face of a person considering the expressions of the same human face at different instances of time. This methodology is developed combining Eigenface method for feature extraction and modified k-Means clustering for identification of the human face. This method endowed the face recognition without using the conventional distance measure classifiers. Simulation results show that proposed face recognition using perception of k-Means clustering is useful for face images with different facial expressions. normalization, effect of compression with JPEG and Wavelet, eigenvectors selection etc. Further, Hyun-chul Kim et al. [6] deals with "face recognition using the mixtureof-eigenfaces method" uses more than one set of eigenfaces from the expectation maximization learning in the PCA mixture model for representation of face images with large variations. The research carried out by Yan Ma et al. [7] stated that during training process average projection vector of different images for each person is used. Recognition uses two thresholds (acceptance and rejection). The work done by M.A. Rabbani et al. [8] used Median instead of mean and with different distance measures in appearance-based statistical eigenface method.

Fig. 1 Schematic block diagram of the proposed face recognition system
In our proposed work we address a simple yet efficient method for face recognition that considered one of the hurdles of the Eigenface method that uses different distance classifiers. In our travail, deliberating different facial images of a single human face taken together as a cluster, then have applied concept of k-Means clustering for recognition that avoids that exploit a threshold value which is changed under different distance classifiers. This is very sophisticated face recognition algorithm and has comparable performance with conventional eigenface face recognition method.
where M = No. of images in training image set, Ψ= Mean face • Normalized images: where Ф i = i th normalized training image.
where u= [ u 1 ,u 2 ,…..u E ], E<=M, Ω i = face space training images, • Mean of each class: where Ω i k = i th face space image of k th class, £ k = mean of each k th class, C = no. of classes (each person belongs to a separate class), P = no. of images per class.

Recognition of a Probe Image
• Normalized test image: where Г test = vector form of test image, Ф test = normalized test image • Mapped test image: where u= [ u 1 ,u 2 ,…..u E ], Ω test = mapped test image in the face space • Mean with test image: (8) where µ k = mean of each class including test image, Ω i k = represents i th face space image of k th class, C = no. of classes (each person belongs to a separate class), P= no. of images per class.
• Difference of old and new mean : where D k = difference between old and new mean value of k th class • Minimum mean difference: D min = Min ( D k ) , k=1 to C (10) where D min = minimum value among all D k • If D min <= T (where, T is very small) => Ф test ε I

EXPERIMENTAL RESULT
In our experimental purpose, standard ORL face database is used which contains a set of 40 people with 10 images (representing different facial expressions) of each face. For all the cases a subset of images are used as training set and the rest of the images as testing set. Table 1 represents the result of average percentage of success when we have used one image per person of the training image set.

CONCLUSION
Under this new methodology we can avoid the use of various distance measurement technique and computation of correct threshold value which is the core parameter of existing eigenface method. Very little work has been done on avoiding the use of distance classifiers and threshold measurements in Eigenface based method. Previous work [9] has given emphasis on selecting the correct distance measure classifiers. When we apply our new methodology for both the case (Case Study 1 & 2), the success rates are satisfactory. This method provides a simple and fast solution while maintaining standard prediction accuracy.


Linking to Scientific Data: Identity Problems of Unruly and Poorly Bounded Digital Objects

Within information systems, a significant aspect of search and retrieval across information objects, such as datasets, journal articles, or images, relies on the identity construction of the objects. This paper uses identity to refer to the qualities or characteristics of an information object that make it definable and recognizable, and can be used to distinguish it from other objects. Identity, in this context, can be seen as the foundation from which citations, metadata and identifiers are constructed. In recent years the idea of including datasets within the scientific record has been gaining significant momentum, with publishers, granting agencies and libraries engaging with the challenge. However, the task has been fraught with questions of best practice for establishing this infrastructure, especially in regards to how citations, metadata and identifiers should be constructed. These questions suggests a problem with how dataset identities are formed, such that an engagement with the definition of datasets as conceptual objects is warranted. This paper explores some of the ways in which scientific data is an unruly and poorly bounded object, and goes on to propose that in order for datasets to fulfill the roles expected for them, the following identity functions are essential for scholarly publications: (i) the dataset is constructed as a semantically and logically concrete object, (ii) the identity of the dataset is embedded, inherent and/or inseparable, (iii) the identity embodies a framework of authorship, rights and limitations, and (iv) the identity translates into an actionable mechanism for retrieval or reference. The International Journal of Digital Curation is an international journal committed to scholarly excellence and dedicated to the advancement of digital curation across a wide range of sectors. ISSN: 1746-8256 The IJDC is published by UKOLN at the University of Bath and is a publication of the Digital Curation Centre.

Introduction
As scholarship moves towards a new paradigm of data-driven science with increasingly large and complex datasets Gray et al., 2005), we have seen a wide-sweeping push to establish the infrastructure to make research data available in conjunction with journal publications. This push includes major journals, such as Science and Nature; major granting agencies, such as the National Science Foundation and National Institutes of Health; major universities, such as the University of California; and major research institutions, such as the British National Library (Chavan & Ingwersen, 2009;Brase, 2009). Many academic libraries and institutional repositories are poised on the cusp of housing datasets, placing them in a position where they will need to engage with the challenge of defining and managing access to datasets (Baker & Yarmey, 2009). This pressure to archive and then link the scholarly publications to the data has been growing, based on arguments that the data is essential for establishing validity, reproducibility and replicability, in addition to fostering value-added activities, such as democratizing access to publicly funded research and facilitating new kinds of analysis (Bell, Hey, & Szalay, 2009;National Science Board, 2005). There is little question that forming a paradigm for the inclusion of data is critical for scientific records in an age of digital records.
Data, as a digital object to be discovered, accessed and preserved, is a relatively new paradigm for scholarship. As Borgman (2007) notes, "libraries and publishers assure access to publications, but no comparable infrastructure exists for access to data and unpublished resources". While many researchers have been contributing towards the establishment of infrastructure for the curation of datasets, such as data policy, stewardship, provenance tracking, permanent identifiers, metadata, and citations, (Buneman et al., 2006;Hilse & Kothe, 2006;Wallis et al., 2010;Paskin, 2005;Zimmerman, 2007) the process has yet to be formally systematized or institutionally adopted, yielding many nodes struggling to assemble themselves into a functional system. Complex challenges, both social and technical, have continued to impede the process of knitting together these elements (Murray-Rust & Rzepa, 2004;Chavan & Ingwersen, 2009;Pollard & Wilkinson, 2010;Carlson & Anderson, 2007).
One group of data scientists went as far as to observe: "In some respects, the researcher was better off in the days of paper publication and record keeping, where there are well-defined standards for citation and some confidence that the cited data will not change." (Buneman et al., 2006). Finding this comment in one of the major scholarly forums for data engineers and computer scientists is indicative of the deep challenge of incorporating datasets into scholarly publications.
This paper argues that one central aspect of bringing these systems together is dataset identity. Despite the presence of a large amount of work on the data deluge, there has been very little work exploring how the construction of dataset identity should function within the conceptual framework of scholarly publication specifically and the scientific record more generally. As such, this paper focuses on the identity aspects of these information objects in terms of the work identity does within scholarly information systems. In doing so, the discussion intentionally steps back from specific structures and technologies in favor of looking at function. Through this novel way of The International Journal of Digital Curation looking at dataset identity, this paper aims to establish a basis for which data structures and standards can be evaluated for their potential utility within existing and evolving scholarly practices.

Why Focus on Identity?
Identity, as a concept, has a long tradition within mathematics of referring to the qualities or characteristics of an information object that make it definable and recognizable, such that it can be distinguished from other objects. Identity, in this context, can be seen as the foundation from which citations, metadata and identifiers are formed. Dataset identity is used in this paper to refer to the abstract conceptualization of a dataset, under which a person can locate concrete physical and digital objects.
The conceptual understanding of what a dataset is governs how it is represented in an information system, and thus has significant implications for how datasets are managed. In their paper, "What is a Text, Really", DeRose et al (1990) argued that a flawed representation of documents can prohibit real progress in organizing and retrieving documents. They wrote: "The way in which text is represented on a computer affects the kinds of uses to which it can be put by its creator and by subsequent users." The way datasets are constructed as information objects has long term ramifications for discovery and access. Once the identity is defined, both machine and human users must be able to parse the identity correctly, e.g. that two objects are the same, are not the same, are different versions, are derivations of each other, etc. Renear & Dubin (2003) argued for the importance of identity, engaging with document identity conditions, which are "a method for determining whether an object x and an object y are the same object." They concluded: "Identity conditions are arguably an essential feature of any rigorously developed conceptual framework for information modeling." These identity conditions form the logic structures for automating discovery and access. This paper presents aspects of dataset identity that are foundational to their function in a scholarly information system, focusing on the example of constructing dataset identity such that it can be linked to. I argue that the construction of identity is not simply a technical challenge of metadata, permanent URLS, DOIs, or description, but a larger challenge of constructing a model for datasets that folds these irregular, evolving and obsolescing technocentric objects into an established model of scholarship and the scientific record.

Why Look at Linking?
Despite the lack of consensus for how to incorporate datasets within scholarly information systems, it seems reasonable to expect linking to be a major aspect of how datasets are managed, as evidenced by projects like OAI-ORE and Linked Data (Pepe et al., 2009;Bizer et al., 2009). In order to link to the data, significant decisions need to be made as to what parts of the research processes are represented in the linked object, which is, in turn, heavily reliant on the identity construction of the data. The following comments help illustrate the challenges of linking to data: The International Journal of Digital Curation Issue 1, Volume 6 | 2011 "All the publications should link data. They should be interlinking between the archives and the literature. One of the goals of the Virtual Astronomical Observatory is actually to do that, and do it properly. It hasn't happened yet for many reasons. It's actually harder than you would think." (Personal Interview, NASA Data Archive Manager, Data Conservancy NSF award OCI0830976, February 11, 2010).

Example of a Dataset
For illustrative purposes the following short, highly simplified scenario is presented (see Figure 1): An astronomer downloads data from three specific data releases of three different telescope projects, each time querying only a specific narrow section of the sky. She cleans up the data, performing some computational transformations, and ultimately compiles the data into a dataset which she uses for analysis. She iteratively works with the data, producing findings, performing computational analyses, producing findings, until she has four articles. Upon acceptance of one of the articles, she is told she needs to submit/release her data in conjunction with the final manuscript. Which of the multiple digital objects described above does she need to link to? Should she reference the same dataset for all four articles?
Eighteen months after her findings are published, a colleague has questions about how the data was handled in her computations. In order to answer her colleague's questions, the astronomer finds herself having to retrace her own steps, many of which were not fully documented and relied upon scripts that she had not looked at since the article was submitted. Which of these digital objects does the colleague need to verify her work and methods, and are they the same as those for publication (above)?
Two years after her findings are published, in response to the assertion that there may be an error in the mathematical model for the computational scripts used, one of her students is compiling a survey of every article and dataset that relied on those scripts. Supposing that he was able to find the associated data, would he be able to identify which of the above digital objects were implicated, such that he could respond to the assertion?
As this example demonstrates, when attempting to capture research data the question of "what, exactly, are you representing and/or capturing for the scholarly record?" has significant and wide reaching implications for what kinds of activities the derivative digital object will support. In this case, the dataset generated by the research can be considered a compound object, consisting of multiple overlapping datasets, that together with research tools and processes, form the researcher's conceptualization of "the data".  Figure 1 shows the series of versions of datasets created as the researcher moved through different processes, leading to a complex branching of versions further complicated by the fact that these datasets all reside within one digital object (a database). The dashed boxes indicate dataset versions used for various publications. As a result, it is difficult to use a URI for the slices as the database is neither online, nor does it offer the specificity to retrieve those individual slices.

Constructing Dataset Identity for Scholarly Publication
The underlying framework of this paper lies in a synthesis of functions of dataset identity from the complex challenges described in the scholarly literature and encountered in our research. However, this paper takes a novel approach of engaging with the question of dataset identity functions across a distributed scholarly publication system. Based on the understandings of the challenges facing dataset identity construction, this paper argues for four functional requirements: (i) the dataset is constructed as a semantically and logically concrete object; (ii) the identity of the dataset is embedded, inherent and/or inseparable; (iii) the identity embodies a framework of authorship, rights, and limitations; and (iv) the identity translates into an actionable mechanism for retrieval or reference. These four functions are explained in more depth below.
The definition of what a dataset is and how it should function within the context of a structured information schema is not a priori given, even within well-established disciplines and methods (Carlson & Anderson, 2007;Collins, 1998;Cole, 2008;Renear et al., 2010). The unifying aspect of data has been in terms of its evidentiary role within the research process. This suggests that any scholarly information system intending to manage datasets will have to accommodate a great deal of variety, even within a single discipline. These challenges may be reasonably expected as a natural process of establishing information infrastructure. Star and Bowker (1999) argued that this is, by nature, an act of reification of exemplars and cognitive conventions, the process of which frequently yields different conceptualizations across different social groups. Rather than standing immutably, these information objects are "embedded within webs of socially organized, situated practices".

The Dataset is Constructed as a Semantically Concrete Object
In practice, as demonstrated by the example in Figure 1, the term "dataset" often refers to an evolving constellation of databases, files, and associated information as a single information object. Unfortunately, this fuzzy conception of datasets presents a problem, as it can yield multiple overlapping versions of what may be described as the same "thing". Pollard  This paper proposes to address this slippage by treating datasets in terms of a mathematical set. By treating datasets as sets, then the dataset is defined by its members. Should one of the members change, then the new set must, by definition, be assigned a new identity so that the two sets can be successfully distinguished from each other. Establishing and modeling the relationships of these objects is contingent on establishing the identity of the objects.
If scientists are to be able to expect to use these objects as evidence (Collins, 1998;Scheiner, 2004), concreteness is an essential function of the citation. The identity should be formed around these objects with enough specificity (e.g., version or date of the records) such that the citation invokes one and only one, unambiguous, clearly defined dataset. This is not simply a problem of assigning identifiers or metadata, but rather for the purposes of aggregation, computation, verification, reproducibility and replicability the dataset must be defined such that it can yield a concrete search result.

The Identity of the Dataset is Embedded, Inherent and/or Inseparable
Within current scholarly practices surrounding journal articles, conventions within publishing practices embeds the identity of the article within the information object, typically within the first page. For example, the ACM conference proceedings article template shown in Figure 2 embeds identity elements in on the first page (circled) such that the following elements are included within the content: author name, author affiliation, title of paper, conference name, conference date, location, copyright/permission and contact information. Compare this with a common form of tabular data, the spreadsheet (such as a csv or excel file). The spreadsheet, emailed from one scientist to another, easily sheds unembedded aspects of identity in the act of being passed, renamed or downloaded. Gilliland-Swetland (2000) articulated the challenge of provenance for digital objects: "The integrity of the evidential value of materials is ensured by demonstrating an unbroken chain of custody, precisely documenting the aggregation of archival materials." Within the context of data and datasets, this suggests that identity of derivative datasets needs to be captured in such a way that the identity of the data is preserved during download and use. Many types and granularities of technology support functions of this nature, from eScience provenance management systems , (Groth et al., 2008) to embedded metadata headers. Given the wide variety of technologies and formats deployed across the sciences and the relatively small market share for research applications, one can reasonably anticipate a long tail of data that is not going to fall under the domain of officially supported cyberinfrastructure (Heidorn, 2008). However, while identity construction should benefit from advances in technology, such as provenance systems, the lack of such technology should not cripple our ability to construct dataset identities. Therefore, in order to remain identifiable, the identity must be inherent enough to support changes in structures and formats, as well as the changes in context. In summary, within scholarly work there is a value to being able to identify the object across changing contexts, and, as such, there is an argument to make the object tightly coupled with its identity.

The Identity Embodies a Framework of Authorship, Rights, and Limitations
Central to researchers' discussions of data reuse and sharing is the recognition that multiple factors, such as authorship, rights, limitations, trust, and permissions, play a central role in their relationship to data. (Cambell et al., 2002;Carlson & Anderson, 2007;Chin & Lansing, 2004). These constraints can be as complex and exacting as the practices from which they are derived. The limitations may be derived from the technology, such as error and accuracy ranges for a sensor; from social mores, such as privacy protections; or from concerns that the data may be misused to support claims for which the data is misapplied. These concerns may derive from the assumptions or parameterizations of the models, or from the politicization of the science, or both, as demonstrated with climate modeling (Edwards, 1999).
Failure to build structures for recognition into dataset records has the potential to hobble the development of datasets as first class objects, as it is one of the mainstays of academic incentive structures. Authorship and attribution/citation are two of the central measures of scholarly productivity, and yet, current technologies offer little hope to aggregate either of these for datasets . Birnholtz, (2008) while studying large high energy physics (HEP) collaborations at CERN, observed that without a publication or attribution system it was "quite easy to get lost or even crushed in the crowd of a large HEP collaboration. Breakdowns in informal systems of recognition, of course, are not a novel result on their own. What distinguishes the present discussion is the almost complete absence of a formal record to fall back on." In an The International Journal of Digital Curation Issue 1, Volume 6 | 2011 earlier paper describing the difficulties of authorship in large collaborations, Birnholtz (2006) noted: "authorship has multiple functions in the sciences. We can describe these as follows: 1) attributing credit for discoveries to a person or group of people; 2) assigning ownership to this person or persons; and 3) enabling the accrual of reputation." While there are differences in how authorship functions between publications and data, Birnholtz's work suggests that there are critical functions for dataset identity in regards to credit, attribution and reputation.
These aspects of reputation and authorship are foundational to scholarship and should not be ignored for dataset identity. Chavan and Ingwersen (2009) saw this gap as well, lamenting "we lack consistency in data citations, which is sure to provide much needed high visibility to data. It is difficult or impossible given the existing citation metrics system to identify who originally created or added value to a datum".
This becomes particularly difficult for data, as while it is clear that data operates under a framework of rights, authorship, and limitations, it is unclear how authorship, rights and limitations should be applied to something which falls outside of the copyright driven models of the print world. The identity of the dataset must therefore embody functions of a system of authorship, rights, limitations, and permissions in a way that is heritable (meaning that a derivative of a restricted dataset should also be restricted), transparent, and flexible in the face of a changing landscape.

The Identity Translates into an Actionable Mechanism for Retrieval and Citation
Formal, traditional citations for journal articles are an excellent example of presenting an object's identity such that it can be translated into an actionable mechanism for either retrieval or citation. Citations, with their highly structured formats, can be considered a formalized identity for scholarly products. A complete citation should act to uniquely identify the exact document in question, in cases where the document is not uniquely identified, the citation is considered to be flawed. A complete citation may also act as an access key, providing the essential information required to retrieve the document from an information system. As such, it can be said that citations perform identity functions within information systems, even though these functions may not be fully automated within the system. DOIs, persistent URLs and Handles all offer solutions for permanent identifiers and offer many retrieval and citation functions in an elegant way (Hilse & Kothe, 2006). However, these technologies all rely on assigning a URI (Berners-Lee, 2006) to the object, and there are several reasons to believe that URIs, by themselves, will not provide answers to how dataset identities should be constructed. First, the infrastructure for assigning URIs to data is still under development, albeit with considerable investment (Brase, 2009;Paskin, 2005). Second, there are semantic granularity questions based on what level an identifier should be assigned such that they offer reasonable efficiency for the system and a concrete citation (Pollard & Wilkinson, 2010). For example, the dataset may be a subset of data within a digital object, as discussed in Figure 1. The technology that houses the data may not support a uniform manner for automatically returning the exact set (such as the results of a federated search query), or the data may reside solely in an offline location, unincorporated with any public information systems, due to necessary access restrictions, such as privacy or proprietary restrictions.

Conclusions
In recent years we have seen a push for the infrastructure to incorporate data as a first class information object within scholarly information systems. Correspondingly, we are engaged in building and vetting new additions to the scholarly publication infrastructure, which are modular and distributed in nature. Representation of identity is critical in this process, and has implications for every function that depends on identifying the information object, including access, retrieval, metrics and aggregation.
In these early stages of infrastructural adoption it is critical that we engage with broad conceptual questions of what is intended to be retained within these information objects, how data should be represented within the system, and within what kinds of structures before these nodes are hardened into rigid infrastructures, classification schemes and standards. Unfortunately, there are many overlapping and intersecting systems that contribute to the ways in which data are unruly and poorly bounded objects within scholarly work. As such, the challenges of dataset identity are not simple to address. While this paper was able to explore and argue for the functions that dataset identity might accommodate within scholarly publications, it was hampered by the lack of well developed conceptual model for data.
Even though these infrastructural technologies are in motion on a horizon between development and obsolescence, in order to evaluate their utility within a larger information infrastructure, we need to understand and evaluate the expectations we have of these objects in within the system. Those expectations are derived directly from the conceptual object of what a (published) dataset is or should be within the scholarly record, and are not simple questions of technology or infrastructure, but rather a vision of how academic work should be done and recorded. It is therefore unconscionable that these important decisions could potentially be left to software developers and publishers as a purely technological challenge.