Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages

• URL: http://arxiv.org/abs/2303.15669v1
• Date: Tue, 28 Mar 2023 01:26:00 GMT
• Title: Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages
• Authors: Seongyeon Park, Myungseo Song, Bohyung Kim and Tae-Hyun Oh
• Abstract summary: We propose an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. The main idea is to pre-train the model to reconstruct de-warped mel-spectrograms from warped ones. We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios.
• Score: 15.32264927462068
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. However, collecting such large-scale transcribed data is expensive. This paper proposes an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. With our pre-training, we can remarkably reduce the amount of paired transcribed data required to train the model for the target downstream TTS task. The main idea is to pre-train the model to reconstruct de-warped mel-spectrograms from warped ones, which may allow the model to learn proper temporal assignment relation between input and output sequences. In addition, we propose a data augmentation method that further improves the data efficiency in fine-tuning. We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios, achieving outstanding performance compared to competing methods. The code and audio samples are available at: https://github.com/cnaigithub/SpeechDewarping

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