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arXiv · 2304.11547

SAR: Self-Supervised Anti-Distortion Representation for End-To-End Speech Model

Jianzong Wang·Xulong Zhang·Haobin Tang·Aolan Sun·Ning Cheng·Jing Xiao
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Abstract

In recent Text-to-Speech (TTS) systems, a neural vocoder often generates speech samples by solely conditioning on acoustic features predicted from an acoustic model. However, there are always distortions existing in the predicted acoustic features, compared to those of the groundtruth, especially in the common case of poor acoustic modeling due to low-quality training data. To overcome such limits, we propose a Self-supervised learning framework to learn an Anti-distortion acoustic Representation (SAR) to replace human-crafted acoustic features by introducing distortion prior to an auto-encoder pre-training process. The learned acoustic representation from the proposed framework is proved anti-distortion compared to the most commonly used mel-spectrogram through both objective and subjective evaluation.

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Jianzong Wang, Xulong Zhang, Haobin Tang, Aolan Sun, Ning Cheng, Jing Xiao. 2023-04-23. SAR: Self-Supervised Anti-Distortion Representation for End-To-End Speech Model. https://arxiv.org/abs/2304.11547

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