Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

• URL: http://arxiv.org/abs/2106.03153v1
• Date: Sun, 6 Jun 2021 15:34:11 GMT
• Title: Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
• Authors: Dongchan Min, Dong Bok Lee, Eunho Yang, Sung Ju Hwang
• Abstract summary: StyleSpeech is a new TTS model which synthesizes high-quality speech and adapts to new speakers. With SALN, our model effectively synthesizes speech in the style of the target speaker even from single speech audio. We extend it to Meta-StyleSpeech by introducing two discriminators trained with style prototypes, and performing episodic training.
• Score: 63.561944239071615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With rapid progress in neural text-to-speech (TTS) models, personalized speech generation is now in high demand for many applications. For practical applicability, a TTS model should generate high-quality speech with only a few audio samples from the given speaker, that are also short in length. However, existing methods either require to fine-tune the model or achieve low adaptation quality without fine-tuning. In this work, we propose StyleSpeech, a new TTS model which not only synthesizes high-quality speech but also effectively adapts to new speakers. Specifically, we propose Style-Adaptive Layer Normalization (SALN) which aligns gain and bias of the text input according to the style extracted from a reference speech audio. With SALN, our model effectively synthesizes speech in the style of the target speaker even from single speech audio. Furthermore, to enhance StyleSpeech's adaptation to speech from new speakers, we extend it to Meta-StyleSpeech by introducing two discriminators trained with style prototypes, and performing episodic training. The experimental results show that our models generate high-quality speech which accurately follows the speaker's voice with single short-duration (1-3 sec) speech audio, significantly outperforming baselines.

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