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arXiv · 2301.03751

Generative Emotional AI for Speech Emotion Recognition: The Case for Synthetic Emotional Speech Augmentation

Abdullah Shahid·Siddique Latif·Junaid Qadir
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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 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.

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Abdullah Shahid, Siddique Latif, Junaid Qadir. 2023-01-10. Generative Emotional AI for Speech Emotion Recognition: The Case for Synthetic Emotional Speech Augmentation. https://arxiv.org/abs/2301.03751

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