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Hidden Markov model-based speech emotion recognition
Open accessAbstract
We introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared. In the first method, a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity, applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines, and results are achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. In comparison, the judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.
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Cited by
Affective state estimation for human–robot Interaction
Kulic, D., & Croft, E. A. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.904899 · Cited by 230
Error weighted semi-coupled hidden Markov model for audio-visual emotion recognition
Lin, J.-C., Wu, C.-H., & Wei, W.-L. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2171334 · Cited by 121
Revisiting Hidden Markov Models for Speech Emotion Recognition
Mao, S., Tao, D., Zhang, G., Ching, P. C., & Lee, T. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683172 · Cited by 83
Metadata
- Authors
- Schuller, B., Rigoll, G., & Lang, M.
- Venue
- 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
- Publisher
- IEEE
- Published
- 2003-12-22
- Volume
- 2
- Pages
- II-1-4
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W4239447739
- Cited by
- 278
- References
- 0
- FWCI
- 11.85
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Hidden Markov model-based speech emotion recognition. https://app.cochat.ai/papers/10.1109/icassp.2003.1202279
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- Schuller, B., Rigoll, G., & Lang, M. (2003). Hidden Markov model-based speech emotion recognition. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). https://doi.org/10.1109/icassp.2003.1202279
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