期刊
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
卷 -, 期 -, 页码 3502-3506出版社
IEEE
DOI: 10.1109/icassp40776.2020.9054579
关键词
adversarial networks; data augmentation; end-to-end affective computing; emotional speech synthesis
资金
- UK Economic & Social Research Council (UK-ESRC) [HJ-253479]
- Engineering and Physical Sciences Research Council (EPSRC) [2021037]
- Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria
In this paper, we propose an adversarial network implementation for speech emotion conversion as a data augmentation method, validated by a multi-class speech affect recognition task. In our setting, we do not assume the availability of parallel data, and we additionally make it a priority to exploit as much as possible the available training data by adopting a cycle-consistent, class-conditional generative adversarial network with an auxiliary domain classifier. Our generated samples are valuable for data augmentation, achieving a corresponding 2% and 6% absolute increase in Micro- and Macro-F1 compared to the baseline in a 3-class classification paradigm using a deep, end-to-end network. We finally perform a human perception evaluation of the samples, through which we conclude that our samples are indicative of their target emotion, albeit showing a tendency for confusion in cases where the emotional attribute of valence and arousal are inconsistent.
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