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We introduce a technique for augmenting neural text-to-speech (TTS) withlowdimensional trainable speaker embeddings to generate different voices from asingle model. As a starting point, we show improvements over the twostate-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 andTacotron. We introduce Deep Voice 2, which is based on a similar pipeline withDeep Voice 1, but constructed with higher performance building blocks anddemonstrates a significant audio quality improvement over Deep Voice 1. Weimprove Tacotron by introducing a post-processing neural vocoder, anddemonstrate a significant audio quality improvement. We then demonstrate ourtechnique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotronon two multi-speaker TTS datasets. We show that a single neural TTS system canlearn hundreds of unique voices from less than half an hour of data perspeaker, while achieving high audio quality synthesis and preserving thespeaker identities almost perfectly.

Deep Voice 2: Multi-Speaker Neural Text-to-Speech