TY - GEN
T1 - Zero-Shot Speech Emotion Recognition Using Generative Learning with Reconstructed Prototypes
AU - Xu, Xinzhou
AU - Deng, Jun
AU - Zhang, Zixing
AU - Yang, Zhen
AU - Schuller, Bjorn W.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Zero-shot Speech Emotion Recognition (SER) enables machines to perceive unseen-emotional speech without knowing any samples from these emotional states, which is helpful in audio-based autonomous affective computing. However, existing works on zero-shot SER directly employ original prototypes and only consider inter-domain knowledge transfer through learning unseen-emotional classifiers. In this regard, we propose a zero-shot SER approach using generative learning with reconstructed prototypes in this paper. Within the proposed approach, we first reconstruct prototypes using the alignment from paralinguistic features to semantic prototypes. Then, generative learning is performed to build the connection from the reconstructed prototypes to the features. Afterwards, zero-shot experiments on emotional-speech data demonstrate that the proposed approach achieves better performance compared with the state-of-the-art approaches.
AB - Zero-shot Speech Emotion Recognition (SER) enables machines to perceive unseen-emotional speech without knowing any samples from these emotional states, which is helpful in audio-based autonomous affective computing. However, existing works on zero-shot SER directly employ original prototypes and only consider inter-domain knowledge transfer through learning unseen-emotional classifiers. In this regard, we propose a zero-shot SER approach using generative learning with reconstructed prototypes in this paper. Within the proposed approach, we first reconstruct prototypes using the alignment from paralinguistic features to semantic prototypes. Then, generative learning is performed to build the connection from the reconstructed prototypes to the features. Afterwards, zero-shot experiments on emotional-speech data demonstrate that the proposed approach achieves better performance compared with the state-of-the-art approaches.
KW - Speech emotion recognition
KW - emotional prototypes
KW - generative learning
KW - zero-shot learning
UR - https://www.scopus.com/pages/publications/85177560192
U2 - 10.1109/ICASSP49357.2023.10094888
DO - 10.1109/ICASSP49357.2023.10094888
M3 - Conference contribution
AN - SCOPUS:85177560192
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -