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Learning emotion embedding from reference audio is a straightforward approachfor multi-emotion speech synthesis in encoder-decoder systems. But how to getbetter emotion embedding and how to inject it into TTS acoustic model moreeffectively are still under investigation. In this paper, we propose aninnovative constraint to help VAE extract emotion embedding with better clustercohesion. Besides, the obtained emotion embedding is used as query to aggregatelatent representations of all encoder layers via attention. Moreover, thequeries from encoder layers themselves are also helpful. Experiments prove theproposed methods can enhance the encoding of comprehensive syntactic andsemantic information and produce more expressive emotional speech.

Improving Emotional Speech Synthesis by Using SUS-Constrained VAE and Text Encoder Aggregation