Emotion recognition from speech: A review

International Journal of Speech Technology

Volumn 15, Issue 2, 2012, Pages 99-117

  Koolagudi, Shashidhar G ^a   Rao, K Sreenivasa ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Classification models; Elicited speech corpus; Emotion recognition; Excitation source features; Natural speech corpus; Prosodic features; Simulated emotional speech corpus; System features

Indexed keywords

CLASSIFICATION MODELS; EMOTION RECOGNITION; EMOTIONAL SPEECH; EXCITATION SOURCES; NATURAL SPEECH; PROSODIC FEATURES; SPEECH CORPORA; SYSTEM FEATURES;

COMPUTER SIMULATION; RESEARCH; SOFTWARE AGENTS; SPEECH ANALYSIS; SPEECH PROCESSING;

SPEECH RECOGNITION;

EID: 84864692637     PISSN: 13812416     EISSN: 15728110     Source Type: Journal    
DOI: 10.1007/s10772-011-9125-1     Document Type: Review

References (122)

1. Alpert, M., Pouget, E. R., & Silva, R. R. (2001). Reflections of depression in acoustic measures of the patient's speech. Journal of Affective Disorders, 66, 59-69. (Pubitemid 32787046)
2. Ambrus, D. C. (2000). Collecting and recording of an emotional speech database. Tech. rep., Faculty of Electrical Engineering, Institute of Electronics, Univ. of Maribor.
3. Ananthapadmanabha, T. V., & Yegnanarayana, B. (1979). Epoch extraction from linear prediction residual for identification of closed glottis interval. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27, 309-319.
4. Atal, B. S. (1972). Automatic speaker recognition based on pitch contours. The Journal of the Acoustical Society of America, 52(6), 1687-1697.
5. Ayadi, M. E., Kamel, M. S., & Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition, 44, 572-587.
6. 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).
7. Bajpai, A., & Yegnanarayana, B. (2008). Combining evidence from sub-segmental and segmental features for audio clip classification. In IEEE region 10 conference TENCON, India, Nov. 2008 (pp. 15). IIIT, Hyderabad.
8. Banziger, T., & Scherer, K. R. (2005). The role of intonation in emotional expressions. Speech Communication, 46, 252-267. (Pubitemid 40952515)
9. Bapineedu, G., Avinash, B., Gangashetty, S. V., & Yegnanarayana, B. (2009). Analysis of lombard speech using excitation source information. In INTERSPEECH-09, Brighton, UK, 6-10 September 2009 (pp. 1091-1094).
10. Batliner, A., Buckow, J., Niemann, H., Noth, E., & Warnke, V. (2000). Verbmobile Foundations of speech to speech translation. Berlin: Springer.
11. Batliner, A., Hacker, C., Steidl, S., Noth, E., Archy, D. S., Russell, M., & Wong, M. (2004). You stupid tin box children interacting with the Aibo robot: A cross-linguistic emotional speech corpus. In Proc. language resources and evaluation (LREC 04), Lisbon.
12. Batliner, A., Biersacky, S., & Steidl, S. (2006). The prosody of pet robot directed speech: Evidence from children. In Speech prosody 2006, Dresden (pp. 1-4).
13. Benesty, J., Sondhi, M. M., & Huang, Y. (Eds.) (2008). Springer handbook on speech processing. Berlin: Springer.
14. Bitouk, D., Verma, R., & Nenkova, A. (2010, in press). Class-level spectral features for emotion recognition. Speech Communication.
15. 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, Sept. 6-10, 2009 (pp. 324327).
16. 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).
17. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., & Weiss, B. (2005). A database of German emotional speech. In Interspeech.
18. Cahn, J. E. (1990). The generation of affect in synthesized speech. In JAVIOS, Jul. 1990 (pp. 1-19).
19. Caldognetto, E. M., Cosi, P., Drioli, C., Tisato, G., & Cavicchio, F. (2004). Modifications of phonetic labial targets in emotive speech: Effects of the co-production of speech and emotions. Speech Communication, 44(1-4), 173-185.
20. Charles, F., Pizzi, D., Cavazza, M., Vogt, T., & Andr, E. (2009). Emoemma: Emotional speech input for interactive story telling. In Decker, Sichman, Sierra, & Castelfranchi (Eds.), 8th int. conf. on autonomous agents and multiagent systems (AAMAS 2009), Budapest, Hungary, May 2009 (pp. 1381-1382).
21. Chauhan, A., Koolagudi, S. G., Kafley, S., & Rao, K. S. (2010). Emotion recognition using lp residual. In IEEE TechSym 2010, West Bengal, India, April 2010. IIT Kharagpur: IEEE.
22. Cowie, R., & Cornelius, R. R. (2003). Describing the emotional states that are expressed in speech. Speech Communication, 40, 5-32.
23. Cowie, R., & Douglas-Cowie, E. (1996). Automatic statistical analysis of the signal and prosodic signs of emotion in speech. In Fourth international conference on spoken language processing ICSLP 96, Philadelphia, PA, USA, October 1996 (pp. 1989-1992).
24. 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.
25. Dellaert, F., Polzin, T., & Waibel, A. (1996). Recognising emotions in speech. In ICSLP 96, Oct. 1996.
26. 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).
27. Douglas-Cowie, E., Campbell, N., Cowie, R., & Roach, P. (2003). Emotional speech: Towards a new generation of databases. Speech Communication, 40, 33-60.
28. Edgington, M. (1997). Investigating the limitations of concatenative synthesis. In European conference on speech communication and technology (Eurospeech 97), Rhodes/Athens, Greece, 1997 (pp. 593-596).
29. Fernandez, R., & Picard, R. W. (2003). Modeling drivers' speech under stress. Speech Communication, 40, 145-159.
30. France, D. J., Shiavi, R. G., Silverman, S., Silverman, M., & Wilkes, M. (2000). Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Transactions on Biomedical Engineering, 47(7), 829-837 (Pubitemid 30421817)
31. Gobl, C., & Chasaide, A. (2003). The role of voice quality in communicating emotion, mood and attitude. Speech Communication, 40, 189-212.
32. Gonzalez, G. M. (1999). Bilingual computer-assisted psychological assessment: An innovative approach for screening depression in Chicanos/Latinos. Tech. report-39, Univ. Michigan.
33. Grimm, M., Kroschel, K., & Narayanan, S. (2008). The Vera am Mittag German audio-visual emotional speech database. In IEEE international conference multimedia and expo, Hanover, Apr. 2008 (pp. 865-868).
34. Hansen, J., & Cairns, D. (1995). Icarus: Source generator based realtime recognition of speech in noisy stressful and lombard effect environments. Speech Communication, 16(4), 391-422.
35. Hoque, M. E., Yeasin, M., & Louwerse, M. M. (2006). Robust recognition of emotion from speech. In Intelligent virtual agents. Lecture notes in computer science (pp. 42-53). Berlin: Springer. (Pubitemid 44532602)
36. 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.
37. I,A. I., & Scordilis, M. S. (2001). Spoken emotion recognition using glottal symmetry. EURASIP Journal on Advances in Signal Processing, 1(11).
38. Iida, A., Campbell, N., Higuchi, F., & Yasumura, M. (2003). A corpus-based speech synthesis system with emotion. Speech Communication, 40, 161-187.
39. 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.
40. 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).
41. Iriondo, I., Guaus, R., Rodrguez, A., Lzaro, P., Montoya, N., Blanco, J. M., Bernadas, D., Oliver, J. M., Tena, D., & Longhi, L. (2000). Validation of an acoustical modeling of emotional expression in Spanish using speech synthesis techniques. In ITRW on speech and emotion, New Castle, Northern Ireland, UK, Sept. 2000.
42. Kamaruddin, N., & Wahab, A. (2009). Features extraction for speech emotion. Journal of Computational Methods in Science and Engineering, 9(9), 1-12.
43. Kao, Y. H., & Lee, L. S. (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).
44. Kodukula, S. R. M. (2009). Significance of excitation source information for speech analysis. PhD thesis, Dept. of Computer Science, IIT, Madras.
45. Koolagudi, S. G., & Rao, K. S. (2010). Real life emotion classification using VOP and pitch based spectral features. In INDICON, (Kolkata, INDIA), Jadavpur University. New York: IEEE Press.
46. 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.
47. Koolagudi, S. G., Reddy, R., & Rao, K. S. (2010). Emotion recognition from speech signal using epoch parameters. In International conference on signal processing and communications (SPCOM), IISc, Bangalore, India, July 2010 (pp. 1-5). New York: IEEE Press.
48. 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, 6-10 September 2009 (pp. 1591-1594).
49. Kwon, O., Chan, K., Hao, J., & Lee, T. (2003). Emotion recognition by speech signals. In Eurospeech, Geneva (pp. 125-128).
50. Lee, C. M., & Narayanan, S. S. (2005). Toward detecting emotions in spoken dialogs. IEEE Transactions on Audio, Speech, and Language Processing, 13, 293-303. (Pubitemid 40320247)
51. Lee, C. M., Narayanan, S., & Pieraccini, R. (2001). Recognition of negative emotion in the human speech signals. In Workshop on automatic speech recognition and understanding, Dec. 2001.
52. 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).
53. Luengo, I., Navas, E., Hernez, I., & Snchez, J. (2005). Automatic emotion recognition using prosodic parameters. In INTERSPEECH, Lisbon, Portugal, Sept. 2005 (pp. 493-496). (Pubitemid 43908107)
54. 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 Press. (Pubitemid 47178301)
55. Makarova, V., & Petrushin, V. A. (2002). RUSLANA: A database of Russian emotional utterances. In International conference on spoken language processing (ICSLP 02) (pp. 2041-2044).
56. Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561-580.
57. 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.
58. McMahon, E., Cowie, R., Kasdeiidis, S., Taylor, J., & Kollias, S. (2003). What chance that a DC could recognize hazardous mental states from sensor inputs? In Tales ofthe disappearing computer, Santorini, Greece.
59. Montro, J. M., Gutterrez-Arriola, J., Colas, J., Enriquez, E., & Pardo, J. M. (1999). Analysis and modeling of emotional speech in Spanish. In Proc. int. conf. on phonetic sciences (pp. 957-960).
60. 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.
61. 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.
62. Murray, I. R., Arnott, J. L., & Rohwer, E. A. (1996). Emotional stress in synthetic speech: Progress and future directions. Speech Communication, 20, 85-91. (Pubitemid 126371279)
63. Nakatsu, R., Nicholson, J., & Tosa, N. (2000). Emotion recognition and its application to computer agents with spontaneous interactive capabilities. Knowledge-Based Systems, 13, 497-504. (Pubitemid 32034774)
64. 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).
65. 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, Aug. 1999 (pp. 495-501).
66. Nicholson, J., Takahashi, K., & Nakatsu, R. (2000). Emotion recognition in speech using neural networks. Neural Computing & Applications, 11, 290-296. (Pubitemid 33216586)
67. Nordstrand, M., Svanfeldt, G., Granstrom, B., & House, D. (2004). Measurements of articulatory variation in expressive speech for a set of Swedish vowels. Speech Communication, 44, 187-196.
68. Nwe, T. L., Foo, S. W., & Silva, L. C. D. (2003). Speech emotion recognition using hidden Markov models. Speech Communication, 41, 603-623.
69. O'Shaughnessy, D. (1987). Speech communication human and machine. Reading: Addison-Wesley.
70. Oudeyer, P. Y. (2003). The production and recognition of emotions in speech: Features and algorithms. International Journal ofHuman-Computer Studies, 59, 157-183.
71. 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.), ACII. LNCS (pp. 279-285). Berlin: Springer.
72. Pao, T. L., Chen, Y. T., Yeh, J. H., Cheng, Y. M., & Chien, C. S. (2007). LNCS: Vol. 4738. Feature combination for better differentiating anger from neutral in Mandarin emotional speech. Berlin: Springer.
73. Pereira, C. (2000). Dimensions of emotional meaning in speech. In Proc. ISCA workshop on speech and emotion, Belfast, Northern Ireland, 2000 (pp. 25-28).
74. Petrushin, V. (1999). Emotion in speech: Recognition and application to call centres. Artificial neural networks in engineering (ANNIE).
75. 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).
76. Petrushin, V. A. (2000). Emotion recognition in speech signal: Experimental study, development and application. In Proc. int. conf. spoken language processing, Beijing, China.
77. Polzin, T., & Waibel, A. (2000). Emotion sensitive human computer interfaces. In ISCA workshop on speech and emotion, Belfast, 2000 (pp. 201-206).
78. Rabiner, L. R., & Juang, B. H. (1993). Fundamentals of speech recognition. Englewood Cliffs: Prentice-Hall.
79. Rahurkar, M., & Hansen, J. H. L. (2002). Frequency band analysis for stress detection using a Teager energy operator based feature. In Proc. int. conf. on spoken language processing (ICSLP'02) (pp. 2021-2024).
80. Rao, K. S., & Yegnanarayana, B. (2006). Prosody modification using instants of significant excitation. IEEE Transactions on Audio, Speech, and Language Processing, 14, 972-980. (Pubitemid 46547658)
81. Rao, K. S., Prasanna, S. R. M., & Sagar, T. V. (2007a). Emotion recognition using multilevel prosodic information. In Workshop on image and signal processing (WISP-2007), Guwahati, India, Dec. 2007. Guwahati: IIT Guwahati.
82. Rao, K. S., Prasanna, S. R. M., & Yegnanarayana, B. (2007b). Determination of instants of significant excitation in speech using Hilbert envelope and group delay function. IEEE Signal Processing Letters, 14, 762-765. (Pubitemid 47434104)
83. 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.
84. Sagar, T. V. (2007). Characterisation and synthesis of emotions in speech using prosodic features. Master's thesis, Dept. of Electronics and communications Engineering, Indian Institute of Technology Guwahati.
85. Scherer, K. R. (2003). Vocal communication of emotion: A review of research paradigms. Speech Communication, 40, 227-256.
86. Scherer, K. R., Grandjean, D., Johnstone, L. T., & Klasmeyer, T. B. G. (2002). Acoustic correlates of task load and stress. In International conference on spoken language processing ICSLP 02, Colorado, 2002 (pp. 2017-2020).
87. Schroder, M. (2001). Emotional speech synthesis: A review. In Seventh European conference on speech communication and technology, Eurospeech Aalborg, Denmark, Sept. 2001.
88. Schroder, M. (2003). Experimental study of affect bursts. Speech Communication, 40(1-2). Special issue on speech and emotion.
89. Schroder, M., & Cowie, R. (2006). Issues in emotion-oriented computing toward a shared understanding. In Workshop on emotion and computing (HUMAINE).
90. Schroder, M., & Grice, M. (2003). Expressing vocal effort in concate-native synthesis. In International conference on phonetic sciences ICPhS 03, Barcelona.
91. Schroder, M., Cowie, R., Douglas-Cowie, E., Westerdijk, M., & Gielen, S. (2001). Acoustic correlates of emotion dimensions in view of speech synthesis. In 7th European conference on speech communication and technology, Aalborg, Denmark, Sept. 2001.
92. 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. 577580). New York: IEEE Press.
93. 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.
94. Sigmund, M. (2007). Spectral analysis of speech under stress. International Journal of Computer Science and Network Security, 7, 170-172.
95. Slaney, M., & McRoberts, G. (2003). BabyEars: A recognition system for affective vocalizations. Speech Communication, 39, 367-384. (Pubitemid 35432920)
96. 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. 1620, 2002.
97. Thevenaz, P., & Hugli, H. (1995). Usefulness of LPC residue in textindependent speaker verification. Speech Communication, 17, 145157.
98. Tischer, B. (1995). Acoustic correlates ofperceived emotional stress.
99. 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.
100. Ververidis, D., & Kotropoulos, C. (2006). Emotional speech recognition: Resources, features, and methods. Speech Communication, 48, 1162-1181. (Pubitemid 44128615)
101. Ververidis, D., Kotropoulos, C., & Pitas, I. (2004). Automatic emotional speech classification. In ICASSP (pp. I593-I596). New York: IEEE Press.
102. Vidrascu, L., & Devillers, L. (2005). Real-life emotion representation and detection in call centers data. In J. Tao, T. Tan, & R. Picard (Eds.), LNCS: Vol. 3784. ACII (pp. 739-746). Berlin: Springer. (Pubitemid 43763397)
103. Wakita, H. (1976). Residual energy of linear prediction to vowel and speaker recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24, 270-271.
104. Wang, Y., & Guan, L. (2004). An investigation of speech-based human emotion recognition. In IEEE 6th workshop on multimedia signal processing (pp. 15-18). New York: IEEE Press. (Pubitemid 40197122)
105. 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).
106. Werner, S., & Keller, E. (1994). Prosodic aspects of speech. In E. Keller (Ed.), Fundamentals of speech synthesis and speech recognition: Basic concepts, state of the art, the future challenges (pp. 23-40). Chichester: Wiley.
107. Williams, C., & Stevens, K. (1972). Emotions and speech: Some acoustical correlates. The Journal ofthe Acoustical Society ofAmerica, 52(4), 1238-1250.
108. Williams, C. E., & Stevens, K. N. (1981). Vocal correlates of emotional states. Speech Evaluation in Psychiatry, 189-220.
109. Wu, C. H., Chuang, Z. J., & Lin, Y. C. (2006). Emotion recognition from text using semantic labels and separable mixture models. ACM transactions on Asian language information processing (TALIP), 5, 165-182.
110. 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.
111. 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).
112. Yegnanarayana, B., Prasanna, S. R. M., & Rao, K. S. (2002). Speech enhancement using excitation source information. In Proc. IEEE int. conf. acoust., speech, signal processing, Orlando, Florida, USA, May 2002 (Vol. 1, pp. 541-544).
113. 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.
114. 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.
115. Yu, F., Chang, E., Xu, Y. Q., & Shum, H. Y. (2001a). Emotion detection from speech to enrich multimedia content. In Proc. IEEE Pacific Rim conference on multimedia, Beijing (pp. 550-557). (Pubitemid 33352147)
116. Yu, F., Chang, E., Xu, Y. Q., & Shum, H. Y. (2001b). Emotion detection from speech to enrich multimedia content. In Second IEEE Pacific-Rim conference on multimedia, Beijing, China, Oct. 2001.
117. Yuan, J., Shen, L., & Chen, F. (2002). The acoustic realization of anger, fear, joy and sadness in Chinese. In International conference on spoken language processing (ICSLP 02), Denver, Colorado, USA, Sept. 2002 (pp. 2025-2028).
118. Zhang, S. (2008). Emotion recognition in Chinese natural speech by combining prosody and voice quality features. In Sun, et al. (Eds.), Advances in neural networks. Lecture notes in computer science (pp. 457-464). Berlin: Springer.
119. Zhou, G., Hansen, J. H. L., & Kaiser, J. F. (2001). Nonlinear feature based classification of speech under stress. IEEE Transactions on Audio, Speech, and Language Processing, 9, 201-216. (Pubitemid 32286594)
120. Zhou, Y., Sun, Y., Yang, L., & Yan, Y. (2009). Applying articulatory features to speech emotion recognition. In International conference on research challenges in computer science, ICRCCS, 28-29 Dec. 2009 (pp. 73-76).
121. Zhou, Y., Sun, Y., Zhang, J., & Yan, Y. (2009). Speech emotion recognition using both spectral and prosodic features. In International conference on information engineering and computer science, ICIECS, Wuhan, Dec. 19-20, 2009 (pp. 1-4). New York: IEEE Press.
122. Zhu, A., & Luo, Q. (2007). Study on speech emotion recognition system in E-learning. In J. Jacko (Ed.), Human computer interaction, Part III, HCII. LNCS (pp. 544-552). Berlin: Springer.