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Emotion recognition from speech using source, system, and prosodic features

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Abstract

In this work, source, system, and prosodic features of speech are explored for characterizing and classifying the underlying emotions. Different speech features contribute in different ways to express the emotions, due to their complementary nature. Linear prediction residual samples chosen around glottal closure regions, and glottal pulse parameters are used to represent excitation source information. Linear prediction cepstral coefficients extracted through simple block processing and pitch synchronous analysis represent the vocal tract information. Global and local prosodic features extracted from gross statistics and temporal dynamics of the sequence of duration, pitch, and energy values represent the prosodic information. Emotion recognition models are developed using above mentioned features separately, and in combination. Simulated Telugu emotion database (IITKGP-SESC) is used to evaluate the proposed features. The emotion recognition results obtained using IITKGP-SESC are compared with the results of internationally known Berlin emotion speech database (Emo-DB). Autoassociative neural networks, Gaussian mixture models, and support vector machines are used to develop emotion recognition systems with source, system, and prosodic features, respectively. Weighted combination of evidence has been used while combining the performance of systems developed using different features. From the results, it is observed that, each of the proposed speech features has contributed toward emotion recognition. The combination of features improved the emotion recognition performance, indicating the complementary nature of the features.

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References

  • Anjani, A. V. N. S. (2000). Autoassociate neural network models for processing degraded speech. Master’s thesis, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600 036, India.

  • Atal, B. S. (1972). Automatic speaker recognition based on pitch contours. The Journal of the Acoustical Society of America, 52(6), 1687–1697.

    Article  Google Scholar 

  • Bajpai, A., & Yegnanarayana, B. (2004). Exploring features for audio clip classification using LP residual and AANN models. In The international conference on intelligent sensing and information processing 2004 (ICISIP 2004), Chennai, India, Jan. 2004 (pp. 305–310).

    Chapter  Google Scholar 

  • Banziger, T., & Scherer, K. R. (2005). The role of intonation in emotional expressions. Speech Communication, 46, 252–267.

    Article  Google Scholar 

  • Bapineedu, G., Avinash, B., Gangashetty, S. V., & Yegnanarayana, B. (2009). Analysis of lombard speech using excitation source information. In INTERSPEECH-09, Brighton, UK, September 6–10 (pp. 1091–1094).

    Google Scholar 

  • Bitouk, D., Verma, R., & Nenkova, A. (2010). Class-level spectral features for emotion recognition. Speech Communication, 52(7–8), 613–625.

    Article  Google Scholar 

  • Burkhardt, F., & Sendlmeier, W. F. (2000). Verification of acoustical correlates of emotional speech using formant synthesis. In ITRW on speech and emotion, Newcastle, Northern Ireland, UK, Sept. 2000 (pp. 151–156).

    Google Scholar 

  • Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., & Weiss, B. (2005). A database of German emotional speech. In Interspeech.

    Google Scholar 

  • Cahn, J. E. (1990). The generation of affect in synthesized speech. In JAVIOS, Jul. 1990 (pp. 1–19).

    Google Scholar 

  • Cowie, R., & Cornelius, R. R. (2003). Describing the emotional states that are expressed in speech. Speech Communication, 40, 5–32.

    Article  MATH  Google Scholar 

  • Cummings, K. E., & Clements, M. A. (1995). Analysis of the glottal excitation of emotionally styled and stressed speech. The Journal of the Acoustical Society of America, 98, 88–98.

    Article  Google Scholar 

  • Dellaert, F., Polzin, T., & Waibel, A. (1996). Recognising emotions in speech. In ICSLP 96, Oct. 1996.

    Google Scholar 

  • Dellert, F., Polzin, T., & Waibel, A. (1996). Recognizing emotion in speech. In 4th international conference on spoken language processing, Philadelphia, PA, USA, Oct. 1996 (pp. 1970–1973).

    Chapter  Google Scholar 

  • Desai, S., Black, A. W., Yegnanarayana, B., & Prahallad, K. (2010). Spectral mapping using artificial neural networks for voice conversion. IEEE Transactions on Audio, Speech, and Language Processing, 18, 954–964.

    Article  Google Scholar 

  • Diamantaras, K. I., & Kung, S. Y. (1996). Principal component neural networks: Theory and applications. New York: Wiley.

    MATH  Google Scholar 

  • Gupta, C. S. (2003). Significance of source features for speaker recognition. Master’s thesis, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600 036, India.

  • Gupta, C. S., Prasanna, S. R. M., & Yegnanarayana, B. (2002). Autoassociative neural network models for online speaker verification using source features from vowels. In Int. joint conf. neural networks, Honolulu, Hawaii, USA, May 2002.

    Google Scholar 

  • hao Kao, Y., & shan Lee, L. (2006). Feature analysis for emotion recognition from Mandarin speech considering the special characteristics of Chinese language. In INTERSPEECH—ICSLP, Pittsburgh, Pennsylvania, Sept. 2006 (pp. 1814–1817).

    Google Scholar 

  • Haykin, S. (1999) Neural networks: A comprehensive foundation. New Delhi: Pearson Education Asia, Inc.

    MATH  Google Scholar 

  • Hua, L. Z., Yu, H., & Hua, W. R. (2005). A novel source analysis method by matching spectral characters of LF model with STRAIGHT spectrum. Berlin: Springer.

    Google Scholar 

  • Iida, A., Campbell, N., Higuchi, F., & Yasumura, M. (2003). A corpus-based speech synthesis system with emotion. Speech Communication, 40, 161–187.

    Article  MATH  Google Scholar 

  • Ikbal, M. S., Misra, H., & Yegnanarayana, B. (1999). Analysis of autoassociative mapping neural networks. In Int. joint conf. neural networks, USA (pp. 854–858).

    Google Scholar 

  • Iliou, T., & Anagnostopoulos, C. N. (2009). Statistical evaluation of speech features for emotion recognition. In Fourth international conference on digital telecommunications, Colmar, France, July 2009 (pp. 121–126).

    Chapter  Google Scholar 

  • Kamaruddin, N., & Wahab, A. (2009). Features extraction for speech emotion. Journal of Computational Methods in Science and Engineering, 9(9), 1–12.

    MATH  Google Scholar 

  • Kishore, S. P., & Yegnanarayana, B. (2001). Online text-independent speaker verification system using autoassociative neural network models. In Int. joint conf. neural networks, Washington, USA, Aug. 2001 (Vol. 2, pp. 1548–1553).

    Google Scholar 

  • Koolagudi, S. G., Maity, S., Kumar, V. A., Chakrabarti, S., & Rao, K. S. (2009). IITKGP-SESC: Speech database for emotion analysis. Communications in computer and information science, LNCS. Berlin: Springer.

    Google Scholar 

  • Koolagudi, S. G., & Rao, K. S. (2009). Exploring speech features for classifying emotions along valence dimension. In S. Chandhury, et al. (Eds.), LNCS. The 3rd international conference on pattern recognition and machine intelligence (PReMI-09), IIT Delhi, December 2009 (pp. 537–542). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Kumar, K. S., Reddy, M. S. H., Murty, K. S. R., & Yegnanarayana, B. (2009). Analysis of laugh signals for detecting in continuous speech. In INTERSPEECH-09, Brighton, UK, September 6–10 (pp. 1591–1594).

    Google Scholar 

  • Lee, C. M., & Narayanan, S. S. (2005). Toward detecting emotions in spoken dialogs. IEEE Transactions on Speech and Audio Processing, 13, 293–303.

    Article  Google Scholar 

  • Liu, J. H. L., & Palm, G. (1997). On the use of features from prediction residual signal in speaker recognition. In European conf. speech processing and technology (EUROSPEECH) (pp. 313–316).

    Google Scholar 

  • Luengo, I., Navas, E., Hernez, I., & Snchez, J. (2005). Automatic emotion recognition using prosodic parameters. In INTERSPEECH, Lisbon, Portugal, Sept. 2005 (pp. 493–496).

    Google Scholar 

  • Lugger, M., & Yang, B. (2007). The relevance of voice quality features in speaker independent emotion recognition. In ICASSP, Honolulu, Hawaii, USA, May 2007 (pp. IV17–IV20). New York: IEEE.

    Google Scholar 

  • Mary, L., & Yegnanarayana, B. (2004). Autoassociative neural network models for language identification. In International conference on intelligent sensing and information processing, Aug. 24 2004 (pp. 317–320). New York: IEEE.

    Chapter  Google Scholar 

  • McGilloway, S., Cowie, R., Douglas-Cowie, E., Gielen, S., Westerdijk, M., & Stroeve, S. (2000). Approaching automatic recognition of emotion from voice: A rough benchmark. In ISCA workshop on speech and emotion, Belfast.

    Google Scholar 

  • Mohan, C. K., & Yegnanarayana, B. (2008). Classification of sport videos using edge-based features and autoassociative neural network models. Signal, Image and Video Processing, 4, 61–73.

    Article  Google Scholar 

  • Mubarak, O. M., Ambikairajah, E., & Epps, J. (2005). Analysis of an MFCC-based audio indexing system for efficient coding of multimedia sources. In 8th international symposium on signal processing and its applications, Sydney, Australia, Aug. 2005.

    Google Scholar 

  • Murray, I. R., & Arnott, J. L. (1995). Implementation and testing of a system for producing emotion by rule in synthetic speech. Speech Communication, 16, 369–390.

    Article  Google Scholar 

  • Murray, I. R., Arnott, J. L., & Rohwer, E. A. (1996). Emotional stress in synthetic speech: Progress and future directions. Speech Communication, 20, 85–91.

    Article  Google Scholar 

  • Murty, K. S. R., & Yegnanarayana, B. (2008). Epoch extraction from speech signals. IEEE Transactions on Audio, Speech, and Language Processing, 16, 1602–1613.

    Article  Google Scholar 

  • Muta, H., Baer, T., Wagatsuma, K., Muraoka, T., & Fukuda, H. (1988). Pitch synchronous analysis of hoarseness in running speech. The Journal of the Acoustical Society of America, 84, 1292–1301.

    Article  Google Scholar 

  • Neiberg, D., Elenius, K., & Laskowski, K. (2006). Emotion recognition in spontaneous speech using GMMs. In INTERSPEECH—ICSLP. Pittsburgh, Pennsylvania, 17–19 September 2006 (pp. 809–812).

    Google Scholar 

  • Nwe, T. L., Foo, S. W., & Silva, L. C. D. (2003). Speech emotion recognition using hidden Markov models. Speech Communication, 41, 603–623.

    Article  Google Scholar 

  • Oudeyer, P. Y. (2003). The production and recognition of emotions in speech: features and algorithms. International Journal of Human-Computer Studies, 59, 157–183.

    Article  Google Scholar 

  • Pao, T. L., Chen, Y. T., Yeh, J. H., & Liao, W. Y. (2005). Combining acoustic features for improved emotion recognition in Mandarin speech. In J. Tao, T. Tan, & R. Picard (Eds.), LNCS. ACII, Berlin, Heidelberg (pp. 279–285), Berlin: Springer.

    Google Scholar 

  • Pao, T. L., Chen, Y. T., Yeh, J. H., Cheng, Y. M., & Chien, C. S. (2007). Feature combination for better differentiating anger from neutral in mandarin emotional speech. In LNCS: Vol. 4738. ACII 2007. Berlin: Springer.

    Google Scholar 

  • Prasanna, S. R. M., Reddy, B. V. S., & Krishnamoorthy, P. (2009). Vowel onset point detection using source, spectral peaks, and modulation spectrum energies. IEEE Transactions on Audio, Speech, and Language Processing, 17, 556–565.

    Article  Google Scholar 

  • Rao, K. S. (2005). Acquisition and incorporation prosody knowledge for speech systems in Indian languages. PhD thesis, Dept. of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India, May 2005.

  • Rao, K. S., & Yegnanarayana, B. (2009). Intonation modeling for Indian languages. Computer Speech and Language, 23, 240–256.

    Article  Google Scholar 

  • Rao, K. S., Prasanna, S. R. M., & Yegnanarayana, B. (2007). Determination of instants of significant excitation in speech using Hilbert envelope and group delay function. IEEE Signal Processing Letters, 14, 762–765.

    Article  Google Scholar 

  • Rao, K. S., Reddy, R., Maity, S., & Koolagudi, S. G. (2010). Characterization of emotions using the dynamics of prosodic features. In International conference on speech prosody, Chicago, USA, May 2010.

    Google Scholar 

  • Reddy, K. S. (2004). Source and system features for speaker recognition. Master’s thesis, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600 036, India.

  • Scherer, K. R. (2003). Vocal communication of emotion: A review of research paradigms. Speech Communication, 40, 227–256.

    Article  MATH  Google Scholar 

  • Schroder, M. (2001). Emoptional speech synthesis: A review. In Seventh European conference on speech communication and technology, Eurospeech, Aalborg, Denmark, Sept. 2001.

    Google Scholar 

  • Schroder, M., & Cowie, R. (2006). Issues in emotion-oriented computing toward a shared understanding. In Workshop on emotion and computing (HUMAINE).

    Google Scholar 

  • Seshadri, G. P., & Yegnanarayana, B. (2009). Perceived loudness of speech based on the characteristics of glottal excitation source. The Journal of the Acoustical Society of America, 126, 2061–2071.

    Article  Google Scholar 

  • Sigmund, M. (2007). Spectral analysis of speech under stress. International Journal of Computer Science and Network Security, 7, 170–172.

    Google Scholar 

  • Tato, R., Santos, R., & Pardo, R. K. J. (2002). Emotional space improves emotion recognition. In 7th international conference on spoken language processing, Denver, Colorado, USA, Sept. 16–20 2002.

    Google Scholar 

  • Theodoridis, S., & Koutroumbas, K. (2006). Pattern recognition (3rd ed.). New York: Elsevier, Academic Press.

    MATH  Google Scholar 

  • Thevenaz, P., & Hugli, H. (1995). Usefulness of LPC residue in textindependent speaker verification. Speech Communication, 17, 145–157.

    Article  Google Scholar 

  • Ververidis, D., & Kotropoulos, C. (2006). A state of the art review on emotional speech databases. In Eleventh Australasian international conference on speech science and technology, Auckland, New Zealand, Dec. 2006.

    Google Scholar 

  • Ververidis, D., Kotropoulos, C., & Pitas, I. (2004). Automatic emotional speech classification. In ICASSP (pp. I593–I596). New York: IEEE.

    Google Scholar 

  • Wakita, H. (1976). Residual energy of linear prediction to vowel and speaker recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24, 270–271.

    Article  Google Scholar 

  • Wang, Y., Du, S., & Zhan, Y. (2008). Adaptive and optimal classification of speech emotion recognition. In Fourth international conference on natural computation, Oct. 2008 (pp. 407–411).

    Chapter  Google Scholar 

  • Williams, C. E., & Stevens, K. N. (1981). Vocal correlates of emotional states. In Speech evaluation in psychiatry (pp. 189–220).

    Google Scholar 

  • Yegnanarayana, B. (1999). Artificial neural networks. New Delhi: Prentice-Hall.

    Google Scholar 

  • Yegnanarayana, B., & Kishore, S. P. (2002). AANN an alternative to GMM for pattern recognition. Neural Networks, 15, 459–469.

    Article  Google Scholar 

  • Yegnanarayana, B., Murthy, P. S., Avendano, C., & Hermansky, H. (1998). Enhancement of reverberant speech using lp residual. In IEEE international conference on acoustics, speech and signal processing, Seattle, WA, USA, May 1998 (Vol. 1, pp. 405–408). New York: IEEE Xplore.

    Google Scholar 

  • Yegnanarayana, B., Reddy, K. S., & Kishore, S. P. (2001a). Source and system features for speaker recognition using aann models. In IEEE int. conf. acoust., speech, and signal processing, Salt Lake City, UT, May 2001.

    Google Scholar 

  • Yegnanarayana, B., Reddy, K. S., & Kishore, S. P. (2001b). Source and system features for speaker recognition using AANN models. In Proc. IEEE int. conf. acoust., speech, signal processing, Salt Lake City, Utah, USA, May 2001 (pp. 409–412).

    Google Scholar 

  • Yegnanarayana, B., Swamy, R. K., & Murty, K. S. R. (2009). Determining mixing parameters from multispeaker data using speech-specific information. IEEE Transactions on Audio, Speech, and Language Processing, 17(6), 1196–1207.

    Article  Google Scholar 

  • Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Busso, C., Deng, Z., Lee, S., & Narayanan, S. (2004). An acoustic study of emotions expressed in speech. In Int. conf. on spoken language processing (ICSLP 2004), Jeju Island, Korea, Oct. 2004.

    Google Scholar 

  • Zeng, Y., Wu, H., & Gao, R. (2007). Pitch synchronous analysis method and Fisher criterion based speaker identification. In Third international conference on natural computation, Washington DC, USA (pp. 691–695). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Zhang, S. (2008). Emotion recognition in Chinese natural speech by combining prosody and voice quality features. In Sun, et al. (Eds.), Lecture notes in computer science. Advances in neural networks (pp. 457–464). Berlin: Springer.

    Google Scholar 

  • Zhu, A., & Luo, Q. (2007). Study on speech emotion recognition system in E-learning. In J. Jacko (Ed.), LNCS. Human computer interaction, Part III, HCII (pp. 544–552). Berlin: Springer.

    Google Scholar 

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  1. School of Information Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Shashidhar G. Koolagudi & K. Sreenivasa Rao

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Koolagudi, S.G., Rao, K.S. Emotion recognition from speech using source, system, and prosodic features. Int J Speech Technol 15, 265–289 (2012). https://doi.org/10.1007/s10772-012-9139-3

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