Generative emotional AI for speech emotion recognition: The case for synthetic emotional speech augmentation
Author | Latif, Siddique |
Author | Shahid, Abdullah |
Author | Qadir, Junaid |
Available date | 2023-07-13T05:40:51Z |
Publication Date | 2023 |
Publication Name | Applied Acoustics |
Resource | Scopus |
ISSN | 0003682X |
Abstract | Despite advances in deep learning, current state-of-the-art speech emotion recognition (SER) systems still have poor performance due to a lack of speech emotion datasets. This paper proposes augmenting SER systems with synthetic emotional speech generated by an end-to-end text-to-speech (TTS) system based on an extended Tacotron 2 architecture. The proposed TTS system includes encoders for speaker and emotion embeddings, a sequence-to-sequence text generator for creating Mel-spectrograms, and a WaveRNN to generate audio from the Mel-spectrograms. Extensive experiments show that the quality of the generated emotional speech can significantly improve SER performance on multiple datasets, as demonstrated by a higher mean opinion score (MOS) compared to the baseline. The generated samples were also effective at augmenting SER performance. 2023 Elsevier Ltd |
Language | en |
Publisher | Elsevier |
Subject | Emotional speech synthesis Speech emotion recognition Speech synthesis Tacotron Text-to-speech WaveRNN |
Type | Article |
Volume Number | 210 |
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