Adapting TTS models For New Speakers using Transfer Learning

• URL: http://arxiv.org/abs/2110.05798v1
• Date: Tue, 12 Oct 2021 07:51:25 GMT
• Title: Adapting TTS models For New Speakers using Transfer Learning
• Authors: Paarth Neekhara, Jason Li, Boris Ginsburg
• Abstract summary: Training neural text-to-speech (TTS) models for a new speaker typically requires several hours of high quality speech data. We propose transfer-learning guidelines for adapting high quality single-speaker TTS models for a new speaker, using only a few minutes of speech data.
• Score: 12.46931609726818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training neural text-to-speech (TTS) models for a new speaker typically requires several hours of high quality speech data. Prior works on voice cloning attempt to address this challenge by adapting pre-trained multi-speaker TTS models for a new voice, using a few minutes of speech data of the new speaker. However, publicly available large multi-speaker datasets are often noisy, thereby resulting in TTS models that are not suitable for use in products. We address this challenge by proposing transfer-learning guidelines for adapting high quality single-speaker TTS models for a new speaker, using only a few minutes of speech data. We conduct an extensive study using different amounts of data for a new speaker and evaluate the synthesized speech in terms of naturalness and voice/style similarity to the target speaker. We find that fine-tuning a single-speaker TTS model on just 30 minutes of data, can yield comparable performance to a model trained from scratch on more than 27 hours of data for both male and female target speakers.

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