Zero-Shot Speech Emotion Recognition Using Generative Learning with Reconstructed Prototypes

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 on...

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Bibliographic Details
Published inICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
Main Authors Xu, Xinzhou, Deng, Jun, Zhang, Zixing, Yang, Zhen, Schuller, Bjorn W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.06.2023
Subjects
Acoustics
Affective computing
Emotion recognition
emotional prototypes
generative learning
Prototypes
Semantics
Signal processing
Speech emotion recognition
Speech recognition
zero-shot learning
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10094888