Speech Emotion Recognition Using Combined Multiple Pairwise Classifiers
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-90176-9_16
Reference16 articles.
1. Feng, H., Ueno, S., Kawahara, T.: End-to-end speech emotion recognition combined with acoustic-to-word ASR model. In: Proceedings of Interspeech, pp. 501–505 (2020)
2. Huang, J., Tao, J., Liu, B., Lian, Z.: Learning utterance-level representations with label smoothing for speech emotion recognition. In: Proceedings of Interspeech, pp. 4079–4083 (2020)
3. Jalal, M.A., Milner, R., Hain, T., Moore, R.K.: Removing bias with residual mixture of multi-view attention for speech emotion recognition. In: Proceedings of Interspeech, pp. 4084–4088 (2020)
4. Jalal, M.A., Milner, R., Hain, T.: Empirical interpretation of speech emotion perception with attention based model for speech emotion recognition. In: Proceedings of Interspeech, pp. 4113–4117 (2020)
5. Stuhlsatz, A., Meyer, C., Eyben, F., Zielke1, T., Meier, G., Schuller, B.: Deep neural networks for acoustic emotion recognition: raising the benchmarks. In: Proceedings of ICASSP, pp. 5688–5691 (2011)
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