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In this paper, we analyze the feasibility of applying few-shot learning to speech emotion recognition task (SER).
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Z. Li, F. Zhou, F. Chen, and H. Li, “Meta-sgd: Learning to learn quickly for few-shot learning,” 2017
2017
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J. Li and C. Lee, “Attentive to individual: A multimodal emotion recognition network with personalized attention profile,” in Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019 , G. Kubin and Z. Kacic, Eds. ISCA, 2019, pp. 211–215
2019
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T. Ko, Y. Chen, and Q. Li, “Prototypical networks for small footprint text-independent speaker verification,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 6804–6808
2020
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S. Chopra, P. Mathur, R. Sawhney, and R. R. Shah, “Meta-learning for low-resource speech emotion recognition,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2021, pp. 6259–6263
2021
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