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Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features

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Abstract

In this work, spectral features extracted from sub-syllabic regions and pitch synchronous analysis are proposed for speech emotion recognition. Linear prediction cepstral coefficients, mel frequency cepstral coefficients and the features extracted from high amplitude regions of spectrum are used to represent emotion specific spectral information. These features are extracted from consonant, vowel and transition regions of each syllable to study the contribution of these regions toward recognition of emotions. Consonant, vowel and the transition regions are determined using vowel onset points. Spectral features extracted from each pitch cycle, are also used to recognize emotions present in speech. The emotions used in this study are: anger, fear, happy, neutral and sad. The emotion recognition performance using sub-syllabic speech segments are compared with the results of conventional block processing approach, where entire speech signal is processed frame by frame. The proposed emotion specific features are evaluated using simulated emotion speech corpus, IITKGP-SESC (Indian Institute of Technology, KharaGPur-Simulated Emotion Speech Corpus). The emotion recognition results obtained using IITKGP-SESC are compared with the results of Berlin emotion speech corpus. Emotion recognition systems are developed using Gaussian mixture models and auto-associative neural networks. The purpose of this study is to explore sub-syllabic regions to identify the emotions embedded in a speech signal, and if possible, to avoid processing of entire speech signal for emotion recognition without serious compromise in the performance.

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References

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

    Article  Google Scholar 

  • Bozkurt, E., Erzin, E., Erdem, C. E., & Erdem, A. T. (2009). Improving automatic emotion recognition from speech signals. In 10th annual conference of the international speech communication association (interspeech), Brighton, UK, 6–10 September 2009 (pp. 324–327).

    Google Scholar 

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

    Google Scholar 

  • Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004). Analysis of emotion recognition using facial expressions, speech and multimodal information. In ACM 6th international conference on multimodal interfaces (ICMI 2004), State College, PA, The USA, October 2004.

    Google Scholar 

  • Chen, J., Huang, Y. A., Li, Q., & Paliwal, K. K. (2004). Recognition of noisy speech using dynamic spectral subband centroids. IEEE Signal Processing Letters, 11, 258–261 (February 2004).

    Article  Google Scholar 

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

    Chapter  Google Scholar 

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

    MATH  Google Scholar 

  • Duda, R. O., Hart, P. E., & Stork, D. G. (2004). Pattern classification (2nd ed.). Singapore: Wiley-Interscience.

    Google Scholar 

  • Gangashetty, S. V., Sekhar, C. C., & Yegnanarayana, B. (2004). Detection of vowel on set points in continuous speech using auto-associative neural network models. In INTERSPEECH. New York: IEEE Press.

    Google Scholar 

  • Gangashetty, S. V., Sekhar, C. C., & Yegnanarayana, B. (2005). Spotting multilingual consonant-vowel units of speech using neural network models. In M. Faundez-Zanuy (Ed.), NOLISP (pp. 303–317). Berlin: Springer.

    Google Scholar 

  • 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, Honululu, Hawii, USA, May 2002.

    Google Scholar 

  • Haykin, S. (1999). Neural networks: a comprehensive foundation. New Delhi: Pearson Education Aisa.

    MATH  Google Scholar 

  • Hoque, M. E., Yeasin, M., & Louwerse, M. M. (2006). Robust recognition of emotion from speech. In Lecture notes in computer science. Intelligent virtual agents (pp. 42–53). Berlin: Springer.

    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 

  • Iliev, A. I., Scordilis, M. S., Papa, J. P., & Falco, A. X. (2010). Spoken emotion recognition through optimum-path forest classification using glottal features. Computer Speech and Language, 24(3), 445–460.

    Article  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 (V2), Washington, USA, August 2001 (pp. 1548–1553).

    Google Scholar 

  • Kodukula, S. R. M. (2009). Significance of excitation source information for speech analysis. Ph.D. thesis, Dept. of Computer Science, IIT, Madras (March 2009).

  • Koolagudi, S. G., & Rao, K. S. (2009). Exploring speech features for classifying emotions along valence dimension. In Springer LNCS. The 3rd international conference on pattern recognition and machine intelligence (PReMI-09).

    Google Scholar 

  • Koolagudi, S. G., & Rao, K. S. (2011). Two stage emotion recognition based on speaking rate. International Journal of Speech Technology, 14, 35–48.

    Article  Google Scholar 

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

    Google Scholar 

  • Koolagudi, S. G., Ray, S., & Rao, K. S. (2010). Emotion classification based on speaking rate. In The 3rd international conference on contemporary computing.

    Google Scholar 

  • Kwon, O., Chan, K., Hao, J., & Lee, T. (2003). Emotion recognition by speech signals. In Eurospeech, Geneva (pp. 125–128).

    Google Scholar 

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

    Article  Google Scholar 

  • Mallidi, S. H. R., Prahallad, K., Gangashetty, S. V., & Yegnanarayana, B. (2010). Significance of pitch synchronous analysis for speaker recognition using AANN models. In INTERSPEECH-2010, Makuhari, Japan, September 2010.

    Google Scholar 

  • Mary, L., & Yegnanarayana, B. (2008). Extraction and representation of prosodic features for language and speaker recognition. Speech Communication, 50, 782–796 (April 2008).

    Article  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 

  • 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, August 2005.

    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. (1988a). Pitch synchronous analysis of hoarseness in running speech. The Journal of the Acoustical Society of America, 84, 1292–1301.

    Article  Google Scholar 

  • Muta, H., Baer, T., Wagatsuma, K., Muraoka, T., & Fukudatt, H. (1988b). A pitch-synchronous analysis of hoarseness in running speech. Status report on speech research SR-93/94, Haskins laboratories.

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

    Google Scholar 

  • Nicholson, J., Takahashi, K., & Nakatsu, R. (1999). Emotion recognition in speech using neural networks. In 6th international conference on neural information processing (ICONIP-99), Perth, WA, Australia, August 1999 (pp. 495–501).

    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 (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 

  • Petrushin, V. A. (1999). Emotion in speech: recognition and application to call centers. In Proceedings of the 1999 conference on artificial neural networks in engineering (ANNIE 99).

    Google Scholar 

  • Prasanna, S. R. M., Zachariah, J. M., & Yegnanarayana, B. (2003). Begin-end detection using vowel onset points. In Proceedings workshop on spoken language, TIFR Mumbai, India (January 2003).

    Google Scholar 

  • Prasannaa, S. M., Gupta, C. S., & Yegnanarayana, B. (2006). Extraction of speaker-specific excitation information from linear prediction residual of speech. Speech Communication, 48, 1243–1261.

    Article  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 (May 2009).

    Article  Google Scholar 

  • Rabiner, L. R., & Juang, B. H. (1993). Fundamentals of speech recognition. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Rao, K. S. (2010). Voice conversion by mapping the speaker-specific features using pitch synchronous approach. Computer Speech and Language, 24, 474–494.

    Article  Google Scholar 

  • Rao, K. S. (2011a). Application of prosody models for developing speech systems in Indian languages. International Journal of Speech Technology, 14, 19–33.

    Article  Google Scholar 

  • Rao, K. S. (2011b). Role of neural network models for developing speech systems. Sadhana (Springer), 36, 783–836.

    Google Scholar 

  • Rao, K. S., & Koolagudi, S. G. (2011). Identification of Hindi dialects and emotions using spectral and prosodic features of speech. Journal of Systemics, Cybernetics and Informatics, 9(4), 24–33.

    Google Scholar 

  • Rao, K. S., & Yegnanarayana, B. (2006). Prosody modification using instants of significant excitation. IEEE Transactions on Speech and Audio Processing, 14, 972–980 (May 2006).

    Article  Google Scholar 

  • Rao, K. S., & Yegnanarayana, B. (2009). Duration modification using glottal closure instants and vowel onset points. Speech Communication, 51, 1263–1269.

    Article  Google Scholar 

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

  • Reddy, B. V. S., Rao, K. V., & Prasanna, S. R. M. (2008). Keyword spotting using vowel onset point, vector quantization and hidden Markov modeling based techniques. In TENCON 2008—2008 IEEE region 10 conference, IIIT, Hyderabad. New York: IEEE Press.

    Google Scholar 

  • Schuller, B., Rigoll, G., & Lang, M. (2004). Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture. In Proc. IEEE int. conf. acoust., speech, signal processing (pp. 577–580). New York: IEEE Press.

    Google Scholar 

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

    Google Scholar 

  • Ververidis, D., & Kotropoulos, C. (2006). Emotional speech recognition: resources, features, and methods. Speech Communication, 48, 1162–1181.

    Article  Google Scholar 

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

    Google Scholar 

  • Vuppala, A. K., Rao, K. S., & Chakrabarti, S. (2012a). Improved vowel onset point detection using epoch intervals. International Journal of Electronics and Communications. doi:10.1016/j.aeue.2.

    Google Scholar 

  • Vuppala, A. K., Yadav, J., Chakrabarti, S., & Rao, K. S. (2012b). Vowel onset point detection for low bit rate coded speech. IEEE Transactions on Audio, Speech, and Language Processing, 20, 1894–1903 (August 2012).

    Article  Google Scholar 

  • Wu, S., Falk, T. H., & Chan, W. Y. (2009). Automatic recognition of speech emotion using long-term spectro-temporal features. In 16th international conference on digital signal processing, Santorini-Hellas, 5–7 July 2009 (pp. 1–6). New York: IEEE Press.

    Chapter  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., 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 

  • 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 D.C., USA (pp. 691–695). Los Alamitos: IEEE Comput. Soc.

    Chapter  Google Scholar 

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

    Shashidhar G. Koolagudi & Sreenivasa Rao Krothapalli

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  1. Shashidhar G. Koolagudi
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  2. Sreenivasa Rao Krothapalli
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Correspondence to Sreenivasa Rao Krothapalli.

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Koolagudi, S.G., Krothapalli, S.R. Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features. Int J Speech Technol 15, 495–511 (2012). https://doi.org/10.1007/s10772-012-9150-8

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