Abstract
This paper presents a technique for synthesizing emotional speech based on an emotion-independent model which is called “average emotion” model. The average emotion model is trained using a multi-emotion speech database. Applying a MLLR-based model adaptation method, we can transform the average emotion model to present the target emotion which is not included in the training data. A multi-emotion speech database including four emotions, “neutral”, “happiness”, “sadness”, and “anger”, is used in our experiment. The results of subjective tests show that the average emotion model can effectively synthesize neutral speech and can be adapted to the target emotion model using very limited training data.
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References
Masuko, T., Tokuda, K., Kobayashi, T., Imai, S.: Speech synthesis from HMMs using dynamic features. In: Proc. ICASSP 1996, pp. 389–392 (1996)
Leggetter, C.J., Woodland, P.C.: Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer Speech and Language 9(2), 171–185 (1995)
Masuko, T., Tokuda, K., Kobayashi, T., Imai, S.: Speaker adaptation for HMM-based speech synthesis system using MLLR. In: The Third ESCA/COCOSDA Workshop on Speech Synthesis, November 1998, pp. 273–276 (1998)
Yamagishi, J., Onishi, K., Masuko, T., Kobayashi, T.: Acoustic modeling of speaking styles and emotional expressions in HMM-based speech synthesis. IEICE Trans. Information and Systems E88-D(3), 502–509 (2005)
Yamagishi, J., Tachibana, M., Masuko, T., Kobayashi, T.: Speaking style adaptation using context clustering decision tree for HMM-based speech synthesis. In: Proc. ICASSP 2004, May 2004, vol. 1, pp. 5–8 (2004)
Kawahara, H.: Restructuring speech representations using a pitch-adaptive time frequency smoothing and a instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sound. Speech Communication 27, 187–207 (1999)
Wu, Y.J., Wang, R.H.: HMM-based trainable speech synthesis for Chinese. To appear in Journal of Chinese Information Processing
Qin, L., Wu, Y.-J., Ling, Z.-H., Wang, R.-H.: Improving the performance of HMM-base voice conversion using context clustering decision tree and appropriate regression matrix. To appear in Proc. ICSLP 2006 (2006)
Yamagishi, J., Tamura, M., Masuko, T., Tokuda, K., Kobayashi, T.: A context clustering technique for average voice models. IEICE Trans. Information and Systems E86-D(3), 534–542 (2003)
Tokuda, K., Masuko, T., Miyazaki, N., Kobayashi, T.: Hidden Markov models based on multi-space probability distribution for pitch pattern modeling. In: Proc. ICASSP 1999, March 1999, pp. 229–232 (1999)
Yoshimura, T., Tokuda, K., Masuko, T., Kobayashi, T., Kitamura, T.: Duration modeling for HMM-based speech synthesis. In: Proc. ICSLP 1998, November 1998, vol. 2, pp. 29–32 (1998)
Russell, J.A.: A circumplex model of affect. Journal of Personality and Social Psychology 39, 1161–1178 (1980)
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine 18(1), 32–80 (2001)
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Qin, L., Ling, ZH., Wu, YJ., Zhang, BF., Wang, RH. (2006). HMM-Based Emotional Speech Synthesis Using Average Emotion Model. In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_27
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DOI: https://doi.org/10.1007/11939993_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49665-6
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