Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus

• URL: http://arxiv.org/abs/2203.15447v1
• Date: Tue, 29 Mar 2022 11:26:56 GMT
• Title: Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
• Authors: Minchan Kim, Myeonghun Jeong, Byoung Jin Choi, Sunghwan Ahn, Joun Yeop Lee, Nam Soo Kim
• Abstract summary: Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus. We propose a transfer learning framework for TTS that utilizes a large amount of unlabeled speech dataset for pre-training.
• Score: 10.158584616360669
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect. In this paper, we propose a transfer learning framework for TTS that utilizes a large amount of unlabeled speech dataset for pre-training. By leveraging wav2vec2.0 representation, unlabeled speech can highly improve performance, especially in the lack of labeled speech. We also extend the proposed method to zero-shot multi-speaker TTS (ZS-TTS). The experimental results verify the effectiveness of the proposed method in terms of naturalness, intelligibility, and speaker generalization. We highlight that the single speaker TTS model fine-tuned on the only 10 minutes of labeled dataset outperforms the other baselines, and the ZS-TTS model fine-tuned on the only 30 minutes of single speaker dataset can generate the voice of the arbitrary speaker, by pre-training on unlabeled multi-speaker speech corpus.

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