Adapting TTS models For New Speakers using Transfer Learning

Understand

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.