Deep Voice 2: Multi-speaker Neural Text-to-speech

Abstract

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 almos

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyarik2017deep

Related papers

• Transfer Learning From Speaker Verification To Multispeaker Text-to-speech Synthesis (2018)0.00
• Deep Voice 3: Scaling Text-to-speech With Convolutional Sequence Learning (2017)0.00
• Modeling Multi-speaker Latent Space To Improve Neural TTS: Quick Enrolling New Speaker And Enhancing Premium Voice (2018)0.00
• Combining Speakers Of Multiple Languages To Improve Quality Of Neural Voices (2021)5.24
• Voice Imitating Text-to-speech Neural Networks (2018)0.00
• Voice Cloning: A Multi-speaker Text-to-speech Synthesis Approach Based On Transfer Learning (2021)0.00
• Translatotron 2: High-quality Direct Speech-to-speech Translation With Voice Preservation (2021)0.00
• Taco-vc: A Single Speaker Tacotron Based Voice Conversion With Limited Data (2019)5.24