A study on the efficacy of model pre-training in developing neural text-to-speech system

• URL: http://arxiv.org/abs/2110.03857v1
• Date: Fri, 8 Oct 2021 02:09:28 GMT
• Title: A study on the efficacy of model pre-training in developing neural text-to-speech system
• Authors: Guangyan Zhang, Yichong Leng, Daxin Tan, Ying Qin, Kaitao Song, Xu Tan, Sheng Zhao, Tan Lee
• Abstract summary: This study aims to understand better why and how model pre-training can positively contribute to TTS system performance. It is found that the TTS system could achieve comparable performance when the pre-training data is reduced to 1/8 of its original size.
• Score: 55.947807261757056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the development of neural text-to-speech systems, model pre-training with a large amount of non-target speakers' data is a common approach. However, in terms of ultimately achieved system performance for target speaker(s), the actual benefits of model pre-training are uncertain and unstable, depending very much on the quantity and text content of training data. This study aims to understand better why and how model pre-training can positively contribute to TTS system performance. It is postulated that the pre-training process plays a critical role in learning text-related variation in speech, while further training with the target speaker's data aims to capture the speaker-related variation. Different test sets are created with varying degrees of similarity to target speaker data in terms of text content. Experiments show that leveraging a speaker-independent TTS trained on speech data with diverse text content can improve the target speaker TTS on domain-mismatched text. We also attempt to reduce the amount of pre-training data for a new text domain and improve the data and computational efficiency. It is found that the TTS system could achieve comparable performance when the pre-training data is reduced to 1/8 of its original size.

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