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    Generative emotional AI for speech emotion recognition: The case for synthetic emotional speech augmentation

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    Date
    2023
    Author
    Latif, Siddique
    Shahid, Abdullah
    Qadir, Junaid
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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 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
    DOI/handle
    http://dx.doi.org/10.1016/j.apacoust.2023.109425
    http://hdl.handle.net/10576/45566
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