Deep Voice 2: Multi-Speaker Neural Text-to-Speech
This work improves multi-speaker TTS for applications requiring diverse voice synthesis, but it is incremental as it builds on existing methods like Deep Voice 1 and Tacotron.
The paper tackles the problem of generating multiple voices from a single neural text-to-speech model by introducing low-dimensional trainable speaker embeddings, achieving high audio quality and preserving speaker identities with less than half an hour of data per speaker.
We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.