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Despite advances in deep learning, current state-of-the-art speech emotionrecognition (SER) systems still have poor performance due to a lack of speechemotion datasets. This paper proposes augmenting SER systems with syntheticemotional speech generated by an end-to-end text-to-speech (TTS) system basedon an extended Tacotron architecture. The proposed TTS system includes encodersfor speaker and emotion embeddings, a sequence-to-sequence text generator forcreating Mel-spectrograms, and a WaveRNN to generate audio from theMel-spectrograms. Extensive experiments show that the quality of the generatedemotional speech can significantly improve SER performance on multipledatasets, as demonstrated by a higher mean opinion score (MOS) compared to thebaseline. The generated samples were also effective at augmenting SERperformance.

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