Semi-Supervised Learning Based on Reference Model for Low-resource TTS

• URL: http://arxiv.org/abs/2210.14723v1
• Date: Tue, 25 Oct 2022 07:48:07 GMT
• Title: Semi-Supervised Learning Based on Reference Model for Low-resource TTS
• Authors: Xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao
• Abstract summary: We propose a semi-supervised learning method for neural TTS in which labeled target data is limited. Experimental results show that our proposed semi-supervised learning scheme with limited target data significantly improves the voice quality for test data to achieve naturalness and robustness in speech synthesis.
• Score: 32.731900584216724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most previous neural text-to-speech (TTS) methods are mainly based on supervised learning methods, which means they depend on a large training dataset and hard to achieve comparable performance under low-resource conditions. To address this issue, we propose a semi-supervised learning method for neural TTS in which labeled target data is limited, which can also resolve the problem of exposure bias in the previous auto-regressive models. Specifically, we pre-train the reference model based on Fastspeech2 with much source data, fine-tuned on a limited target dataset. Meanwhile, pseudo labels generated by the original reference model are used to guide the fine-tuned model's training further, achieve a regularization effect, and reduce the overfitting of the fine-tuned model during training on the limited target data. Experimental results show that our proposed semi-supervised learning scheme with limited target data significantly improves the voice quality for test data to achieve naturalness and robustness in speech synthesis.

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