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  • 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 6, November - December (2013), pp. 16-24 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME A STUDY OF ACOUSTIC FEATURES PATTERN OF EMOTION EXPRESSION FOR HINDI SPEECH Sushma Bahuguna1, Y. P. Raiwani2 1 2 Singhaniya University, Pechri, Rajasthan, India Department of Computer Science & Engineering, HNB Garhwal University, Uttarakhand, India ABSTRACT Emotion is an affective state of consciousness that involves feeling and plays a significant role in communication. There are objectively measurable voice parameters at physiological, articulatory - phonetics and acoustic levels that reflect currently experiencing affective emotional state of a person. Measurements at the physiological and articulatory-phonetics levels are invasive and require specialized equipments. Acoustic parameters of emotion expression can be obtained unremarkably from speech recording that allow speech emotion analysis and speaker’s emotion recognition inferences and hold important prospects for interdisciplinary research on emotional speech. In present study we analyzed feature pattern of acoustic parameters including pitch, duration, intensity and Formants based on the emotional speech sentences in Hindi classified according to auditory impressions and emotion based speaker identification system. Six speakers of different age group were chosen and twenty sample sentences in Hindi were recorded in neutral and four types of emotions i.e. Anger, Happiness, Sadness and surprise. We first conducted a listening test of sample sentences to identify speaker’s emotion based on auditory impressions. Then speaker’s emotion identification of sample sentences was done using Mel Frequency Cepestral Coefficient and Vector Quantization techniques and subsequently PRAAT software package was used to analyze feature pattern of acoustic parameters for sample sentences identified correctly by human and machine recognizer. Keywords: Acoustic parameters, Emotion Recognition, MFCC, prosodic Feature, VQ. I. INTRODUCTION Speech emotion recognition is becoming more important as a multi-disciplinary field of research in computer science, psychology, acoustics, health care, speech science, children education etc. Emotion Feature determination is one of the most important aspects in emotion recognition. 16
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Sufficient information about transmitted emotions should be carried by the selected features. In emotional expression the important aspects required are: What information user conveys and how is conveyed. Voice cues are commonly divided into fundamental frequency, vocal perturbation, voice quality, intensity and combination of these aspects i.e. prosodic features [1]. The prosodic characteristics such as rhythm, intonation and stress are manifested in acoustic speech signal in terms of duration, F0 contour and energy contour. An emotional state study based on prosodic features requires knowledge of relationship between emotions and acoustic features and suitable features are needed for emotion recognition. [2] have done important research on acoustic attributes for detecting primary emotions. [3] worked on pitch and energy related features. [4] classified different emotions by using pitch, intensity, speech rate, formant and energy related features. [5] used fundamental frequency, energy and duration features for recognition of emotion. [6] extracted features derived from duration, loudness, pitch and quality features. [7], described MFCC along with LFPC as emotion speech features to recognize emotions. [8] described a method using MFCC and VQ for performing speaker dependent emotion recognition. [9] Have described emotion conversion algorithm for emotion conversion for Hindi speech. [10] used LPCC, MFCC and D-KNN classifier. Analysis of speech features pattern of emotion expression requires correct interpretation of acoustic parameters and analytic techniques [11-13]. Experiments using computer based techniques to explore the aspects of speech that reflect emotions have been conducted by [1] [14-17]. They all agree that the most crucial aspects related to prosody are intensity contour, pitch (F0) contour and the timing of utterances. In our experiment twenty emotional speech sentences were recorded by six (three male and three female) Hindi speakers of different age groups .The non professional speakers were chosen to avoid exaggerated expressions. The database of 600 utterances including short sentences portraying the four emotions namely Anger, Happiness, sadness and surprise as well neutral sentences are classified by human and machine. We first conducted a listening test to examine how much the speakers intended emotions agreed and then agreed utterances were provided to the emotion based speaker identification system [18] for emotion recognition. Finally we selected five sample sentences of each speaker that were correctly recognized with respect to different emotions to study and analyze similarities of patterns in the pitch, intensity, duration and formants using PRAAT software package developed by Boersma and weenink [19]. The speech samples were segmented for quantitative description of relatively comparable and homogeneous parts of utterance and we extracted voice cues of importance to speech emotions including pitch, intensity, duration, relative extent of frequency energy in the spectrum and formants i.e. bandwidth of energy peaks and frequency in the spectrum. The following PRAAT script commands are used to extract parameter values from the speech files. Read from file... 'filesname$' Duration = Get total duration To Pitch... time_step minimum_pitch maximum_pitch startTime = Get start time endTime = Get end time meanpitch = Get mean... 'startTime' 'endTime' Hertz # (1) get the value of the minimum pitch pthmin = Get minimum... 'startTime' 'endTime' Hertz Parabolic pthmax = Get maximum... 'startTime' 'endTime' Hertz Parabolic start=0 end=duration time='duration'/5 newstart='start'+'time' 17
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME To Formant (burg)... 0 5 5500 0.025 50 f = selected("Formant") select 'f' f1 = Get value at time... 1 'newstart' Hertz Linear f2 = Get value at time... 2 'newstart' Hertz Linear f3 = Get value at time... 3 'newstart' Hertz Linear To Intensity... pthmin 0 subtract_mean intdb = Get mean... start end dB II. ANALYSIS AND RESULTS Pitch, an integral part of voice is defined as the rate of vibration of vocal cords which is determined by the length and thickness of vocal cord and tightening and relaxation of the muscles surrounding them. Faster rates form higher pitches while slower rates elicit lower pitches. Besides vocal cord characteristics, pitch pattern is a combination of emotional state and attitude of the speaker. [20] described typically high median pitch for angry speech and [21] discovered highest pitch for vowels for angry speech. Stressed syllable is indicated by a raise or fall in fundamental frequency [22, 23] and Sudden rise in stressed syllables is found in angry speech [24]. [25] found increased pitch mean for happiness as in anger speech. In contrast to excited emotion sadness yield lower mean pitch and narrower range [26, 27]. In our experiment the mean pitch and pitch range values obtained for different emotions from sample sentence “Tum gaye aur wah aa gai”. of different speakers are listed in following tables and bar graphs. [Table 1, 2 and Fig.1, 2]. MeanPitch Mean Pitch(Hz) Emotion S1(f) S2(m) S3(m) S4(f) S5(f) S6(m) angr 301.81 172.8 289.66 346.21 255.11 320.74 Hertz 400 angr 200 hpy hpy 274.95 127.32 216.4 302.57 265.83 284.71 ntrl 225.26 108.69 161.04 234.13 202.17 207.07 S1(f) S2(m) S3(m) S4(f) S5(f) S6(b) sad 178.87 90.012 140.87 218.28 175.66 175.83 Speakers srp 359.9 240.57 324.09 330.83 357.94 324.9 0 ntrl sad srp Fig. 1: Bar graph of Mean Pitch values in different emotions Table 1: Mean Pitch values of speakers in different emotions Pitch pattern vary depending on the emotions expressed by the speaker in various sentences. For anger and surprise emotions, pitch pattern increase as compared to neutral sentences in case of male speakers and significant difference is observed in case of female speakers. Anger and surprise are characterized by high mean pitch and wide range in contrast to sadness which typically lowers activation level and yield lower mean pitch and narrow range. Pitch Range(Hz) Emotion S1(f) S2(m) S3(m) S4(f) PitchRange S5(f) S6(m) 258.52 321.86 194.51 305.97 205.20 387.28 hpy 230.22 310.17 159.26 175.89 186.70 369.17 ntrl 203.48 240.58 103.32 141.32 184.47 324.35 sad 159.34 36.343 93.288 115.76 90.647 243.97 srp 379.24 389.27 218.54 352.31 215.12 Hertz angr 600 400 200 0 angr hpy ntrl S1(f) S2(m) S3(m) S4(f) speakers 392.04 S5(f) S6(m) sad srp Fig. 2: Bar Graph of pitch ranges in different emotions Table 2: Pitch Range Values of speakers in different emotions 18
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME The connotation of the sentence also affect pitch contour as in neutral sentence lower pitch is observed toward the end and pitch pattern raises in interrogative sentence towards the end of the sentence and continuous rising pitch pattern may indicate continuation of speech. The duration characteristics can be analyzed at different levels from segment to sentence level timing, rhythm and speaking rate. [28 -31] noted anger and happiness have higher speaking rate while surprise has normal tempo and sadness with a reduced articulation rate. A set of rules determine correct timing in segmental duration [22]. At sentence level, the speech rate and correct placing of pauses for phrase boundaries are important and phoneme duration differs due to neighboring phonemes. Table 3 and Fig. 3 show that duration responses of anger and surprise emotions are characterized by short duration pattern as compared to the happy and sadness emotions. Duration(Seconds) Duration S1(f) S2(m) S3(m) S4(f) S5(f) S6(m) angr 1.612 1.241 1.156 1.394 0.888 1.554 hpy 2.059 2.065 1.611 1.472 1.579 2.443 ntrl 1.714 1.682 1.453 1.43 1.107 3 Time (Sec) Emotion 1.587 sad 1.817 1.86 1.895 1.838 1.832 2.344 srp 1.697 1.264 1.06 1.429 1.064 angr 2 hpy 1 0 ntrl 1.493 S1(f) S2(m) S3(m) S4(f) S5(f) S6(b) sad Speakers Table 3: Duration values in different emotion for sentence “Tum gaye aur wah aa gai”. srp Fig. 3: Bar graph of duration pattern in different emotions The intensity is the amount of energy flowing per unit time through a unit area perpendicular to the direction of propagation. Vowels are usually more intense than consonants at syllable level. At a phrase level syllables show weaker intensity towards the end of sentence. In speech the intensity pattern is highly related with fundamental frequency. The intensity of sound goes up in proportion to fundamental frequency [22]. [31] found noticeable increased energy envelope in angry speech. Happiness showed similar characteristics reported by [32, 33]. Sadness was associated with decreased intensity [33, 34]. Table 4 and fig. 4 show the characteristics of intensity pattern of different speakers in different emotional state for sentence “Tum Gaye aur wah aa gai”. Anger, Happiness and surprise are characterized by high frequency energy whereas sadness is characterized by low frequency energy. The intensity pattern of all speakers shows higher intensity for high activation levels and lower intensity for low activation levels [Fig.8]. Intensity (dB) Intensity S1(f) S2(m) S3(m) S4(f) S5(f) S6(m) angr 74.91 69.09 81.225 78.44 73.21 71.27 hpy 72.95 66.71 68.022 70.83 68.48 100 dB Emotion 59.55 angr 50 hpy 0 ntrl 64.5 61.67 62.83 69.88 66.28 ntrl 53.6 S1(f) S2(m) S3(m) S4(f) S5(f) S6(b) sad 55.66 59.72 53.435 64.26 57.58 sad 52.05 Speakers srp 75.53 74.33 80.681 79.98 75.49 80.56 srp Fig. 4: Bar graph of Intensity pattern in different emotions Table 4: Intensity Values in different emotions Formants are resonant frequencies produced in the vocal tract and change their center frequency and bandwidth during speech. [21] explained anger produced vowels with a more open vocal tract and form greater mean of first formant frequency than that of neutral speech. [35] lists predictions of formant frequencies along with several emotion classes. Table 5-7 and Fig.5-8 shows formants pattern of speakers in different emotions for sentence “Tum gaye aur wah aa gai”. Neutral sentences display almost uniform formant pattern and glottal vibration pattern in contrast to irregular 19
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME formant contours of sadness, anger and surprise. In anger and surprise sentences first formant frequency has greater mean than neutral state and amplitude of F2 formant and F3 formant are higher with respect to neutral. The bandwidths of the formants in the angry voice samples seemed to be greater for all three formants with only one exception, the second formant for the female and increase in bandwidth is most obvious for the male samples. Happiness is characterized by increased F1 formant bandwidth and decreased F1 formant mean whereas sad sentences are characterized by decreased F1 formant bandwidth and increased F1 formant mean with lower F2 formant mean. For angry and surprise energy concentration is higher in higher bands and for sadness energy concentration is at the lower bands. The rate of change of spectral energy is faster for anger and surprise and slower for sadness. Formant F1 (Hz) S1(f) S2(m) S3(m) S4(f) S5(f) angr 848.9799 687.027 698.4793 600.4342 739.3562 hpy 753.0259 518.5772 549.718 611.9078 413.9065 722.7814 ntrl 588.5144 696.2933 597.6724 601.9406 454.1527 Formant F1 S6(m) 809.5812 690.321 Hertz Emotion 1000 500 0 angr hpy S1(f) S2(m) S3(m) S4(f) sad 713.4554 828.3331 584.9845 596.5827 523.2751 697.0164 862.6901 706.3477 680.7914 521.655 827.0977 S6(b) 626.9803 srp S5(f) Speakers ntrl sad Fig 5 : Bar graph of F1 Formants Pattern in different emotions Table 5: F1 formant values of speakers in different emotions Formant F2 (Hz) Formant F2 S1(f) S2(m) S3(m) S4(f) S5(f) S6(m) angr 2013.08 2053.61 1826.54 1978.24 1153.92 1701.975 hpy 1685.22 1780.44 1764.55 2030.18 990.119 1988.055 ntrl 1551.04 1907.45 1761.3 1817.79 908.646 1940.999 sad 1535.62 1826.99 1748.22 1910.01 964.178 1878.302 srp 1690.67 2179.1 1832.64 2004.66 971.012 1797.742 Hertz Emotion angr 3000 2000 1000 0 hpy S1(f) S2(m) S3(m) S4(f) S5(f) S6(b) ntrl sad Speakers srp Fig 6 : Bar Graph of F2 Formants Pattern in different emotions Table 6: F2 Formants values of speakers in different emotions Formant F3 (Hz) Formant F3 S1(f) S2(m) S3(m) S4(f) S5(f) S6(m) angr 2672.822 2717.579 2758.924 2823.393 1730.086 2714.698 hpy 2348.921 2796.177 2618.948 2804.942 1802.45 2933.827 ntrl 2086.626 2799.162 2679.759 2845.564 1796.637 2971.107 sad 2398.454 2740.446 2608.102 2903.377 1634.801 2755.499 srp 2486.187 3009.025 2815.852 2886.466 1735.941 2960.354 Hertz Emotion 4000 angr 2000 hpy 0 S1(f) S2(m) S3(m) S4(f) S5(f) S6(b) ntrl sad Speakers srp Fig 7: Bar Graph of F3 formants pattern in different emotions Table 7: F3 Formants values of speakers in different emotions The results of acoustic parameters computed on the basis of value and graphical pattern are as follows: Anger sentences reveal average higher mean pitch with high variance. Pitch range and its variation are wider than neutral sentences. The pitch pattern changes abruptly on stressed and pitch contours of all syllables are falling with strong downward infliction at the end of sentences. These sentences are characterized by higher intensity and less duration (Fig.8). First formant mean is increased and F3 formant mean is higher while F2 formant shows higher or lower mean of formant frequency. Surprise sentences are characterized by very high mean pitch with very high variance, much wider pitch range and high intensity. The rhythms are fast with variance of phoneme duration and contours of syllables are falling. 20
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME (i) (ii) Fig. 8: Energy spectrum of utterance in Hindi “Tum gaye aur wah aa gai”: (i) Female Speaker (ii) Male Speaker Figure shows the variation in the pattern of acoustic parameters for different emotions i.e (A - Anger, B - Happy, C -Neutral, D - Sad, E – Surprise) In Sentences pertaining to happiness the mean pitch of the utterance is high, has a high variance, much wider pitch range and pitch changes in smooth upward inflictions. The rhythm is rather fast and contours of all syllables are rising (Fig. 8). These sentences are characterized by 21
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME higher intensity, decreased F1 formant mean, and increased F1 formant bandwidth. Neutral sentences reflect the high mean pitch but less than happiness. The rhythm is slow, with a high variance of phoneme durations. The contours of syllables are rising and uniform formant structure and glottal vibration pattern is noted. Sentences in sad emotion show pattern of low mean pitch, low variance, slightly narrower pitch range and downward inflections in pitch change. The intensity is lower as compared to other emotions and Rhythm is slow with high variance of phoneme durations (Fig.8). The contours of all syllables are falling. The feature pattern of formants is characterized by increased F1 formant mean, decreased F1 formant bandwidth and lower F2 formant mean. III. CONCLUSIONS Analysis results are summarized as: (1) the magnitude of mean pitch increased with increase in degree of emotion. The mean pitch is highest with high variance in the state of surprise and higher with high variance in state of anger. The mean pitch of neutral state is high but less than happiness whereas it is low pitched with little high frequency energy in the state of sad. Due to lower pitch and short duration range neutral and sad sentences are the easiest sentences to recognize. Anger and surprise sentences are less homogeneous and there is great increase in pitch. Happy sentences are characterized by important differences in voice quality. (2) Anger in speech causes increased intensity and happiness causes slightly increased intensity whereas sadness in speech decreases the speech intensity. (3) Surprise and anger states are characterized by fast rhythm with little variance of phoneme durations whereas sadness and neutral state are characterized by slow rhyme with high variance of phoneme durations. In the state of happiness rhythm is rather fast with variance of phoneme duration. (4) First formant frequency for anger sentences has a greater mean than neutral sentences and neutral formant are characterized by uniform formants in contrast to irregular formants of sad, anger and surprise. In present study samples of good acoustic quality were carefully selected and results of acoustic features pattern for recognizing emotions seem to hold promise and these samples may be representative for all our utterances. REFERENCES [1] [2] [3] [4] [5] [6] [7] Patrik N. Juslin et al. 2008, Scholarpedia 3(10):4240 Murray, I. &. Arnott, J.L. (1993). “Towards the Simulation of Emotion in Synthetic Speech: A Review of the Literature on Human Vocal Emotion”. Journal of the Acoustic Society of America, Vol. 93, No. 2, ISSN: 0001- 4966. Schuller et al. 2003 , “Hidden Markov Model-based Speech Emotion Recognition”, Proceedings of International Conference on Acoustics, Speech, and Signal Processing, April 2003, Hong Kong, China. Park et al. 2002, “Emotion Recognition based on Frequency Analysis of Speech Signal”, International Journal of Fuzzy Logic and Intelligent Systems, Vol. 2, No.2, ISSN: 1064-1246. D. Morrison, L.C. De Silva, “Voting ensembles for spoken affect classification”, Journal of Network and Computer Applications 30 (2007). Tato et al.2002, “Emotional Space Improves Emotion Recognition”, Proceedings of International Conference on Spoken Language Processing, pp. 2029-2032, September 2002, Colorado, USA. Nwe et al. 2003, “Speech Emotion Recognition Using Hidden Markov Models”, Speech Communication, Vol. 41, No. 4, ISSN: 0167-6393. 22
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] Le et al. 2004, “Recognizing Emotions for the Audio-Visual Document Indexing”, Proceedings of the Ninth IEEE International Symposium on Computers and Communications, Alexandria, Egypt. S.S. Agrawal et al. 2010, “Transformation of emotion based on acoustic features of intonation patterns for Hindi speech”, African Journal of mathamatics and computer science research vol. 3(10). Tsang-Long Pao, et. al, “Emotion recognition via Continuous Mandarin speech”, Advances in Human-Computer interaction, Tatung University Taiwan,R.O.C Kent,R.D. 1997, “The speech sciences”, san Diego, CA: Singular Press. Titz, I.R. 1994, “Principles of voice production”, Englewood Cliffs, NJ: Prentice-Hall. Owren, M. J. & Bachorowski, J.-A.(2007). “Measuring emotion related vocal acoustics”, Handbook of emotion elicitation and assessment, New York. Oxford University Press. Williams and Stevens, 1972), “Emotions and Speech: Some Acoustic Correlates”, The Journal of the Acoustic Society of America, vol 52 (4) pp. Banse and Sherer, “Acoustic profiles in vocal emotion expression”, Journal of Personality and Social Psychology, 70(3). Burkhardt, F., Sendlmeier, W., 2000. “Verification of acoustical correlates of emotional speech using formant-synthesis”, ISCA Workshop on Speech and Emotion. Pierre-Yves Oudeyar ,2002, “The production and recognition of emotions in speech : features and algorithms”, Int. J. Human- Computer studies 59(2003) 157-183. Sushma Bahuguna & Y. P. Raiwani 2013, “Study of Speaker’s Emotion Identification for Hindi speech”, International Journal on Computer Science and Engineering.Vol 5 no. 7 , 629634 Boersmaand Weenink, http://www.fon.hum.uva.nl/praat/. Fairbanks, G., et. al, 1939. “An experimental study of the pitch characteristics of the voice during the expression of emotion”, Speech Monograph 6, 87–104. Williams, C.E et. al 1972. “Emotions and speech: some acoustical correlates”, Nonverbal Communication: Readings with Commentary, second edition. Oxford University Press, New York. Klatt, D.H. 1987, “Review of test-to-speech conversion for English”, Journal of the Acoustical Society of America, 82. Dormen M.F et. al 1996, “Relative spectral change of formant transactions as cues to labial and alveolar”, Journal of the Acoustical Society of America, 100, 3825-30. Fonagy, I., 1978, “A new method of investigating the perception of prosodic features”, Language and Speech 21, 34–49. Fonagy, I., Magdics, K., 1963, “Emotional patterns in intonation and music”. Kommunikations for sch 16, 293–326. Johnson, W.F., et. al, 1986. “Recognition of emotion from vocal cues”, Arch. Gen. Psych. 43. Oster, A., et. al, 1986, “The identification of the mood of a speaker by hearing impaired listeners”, Speech Transmission Lab. – Q. Prog. Stat. Rep. 4. Dellaert, F., et. al, 1996, “Recognizing emotion in speech”, ICSLP 1996, Philadelphia, PA. Huber, R., et. al,1998. “You beep machine – emotion in automatic speech understanding systems”, Proceedings of the Workshop on Text, Speech, and Dialog. Masark University. Petrushin, V., 2000, “Emotion recognition in speech signal: experimental study, development, and application”. ICSLP 2000, Beijing, China. Ang, J., et. al, 2002, “Prosody-based automatic detection of annoyance and frustration in human–computer dialog”, ICSLP 2002. Fonagy, I., 1981, “Emotions, voice and music”, Sundberg, J. (Eds.), Research Aspects on Singing, Royal Swedish Academy of Music No. 33. 23
  • 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME [33] [34] [35] [36] [37] [38] [39] Davitz, J.R., 1964, “Personality, perceptual, and cognitive correlates of emotional sensitivity”, Davitz, J.R. (Ed.), The Communication of Emotional Meaning. McGraw-Hill, New York. Skinner, E.R., 1935, “A calibrated recording and analysis of the pitch, force and quality of vocal tones expressing happiness and sadness”, Speech Monograph 2. Scherer, K.R., 2003, “Vocal communication of emotion: a review of research paradigms”. Speech Communication 40. Kamlesh Sharma, Dr. T.V. Prasad And Dr. S. V. A. V. Prasad, “Hindi Speech Enabled Windows Application Using Microsoft SAPI”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 425 - 436, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375, Published by IAEME Gunjan Singh, Avinash Pokhriyal and Sushma Lehri, “Fuzzy Rule Based Classification and Recognition of Handwritten Hindi Curve Script”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 337 - 357, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375, Published by IAEME P Mahalakshmi and M R Reddy, “Speech Processing Strategies For Cochlear Prostheses-The Past, Present And Future: A Tutorial Review” International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 3, Issue 2, 2012, pp. 197 - 206, ISSN Print: 0976-6480, ISSN Online: 0976-6499, Published by IAEME. P Mahalakshmi, M R Reddy,, “Cochlear Implant Acoustic Simulation Model Based On Critical Band Filters” International journal of Electronics and Communication Engineering &Technology (IJECET), Volume 3, Issue 3, 2012, pp. 116 - 129, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472, Published by IAEME. 24