High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units

• URL: http://arxiv.org/abs/2306.17005v1
• Date: Thu, 29 Jun 2023 15:02:22 GMT
• Title: High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units
• Authors: Junchen Lu, Berrak Sisman, Mingyang Zhang, Haizhou Li
• Abstract summary: We propose a novel AVO method leveraging the learning objective of self-supervised discrete speech unit prediction. Experimental results show that our proposed method achieves remarkable lip-speech synchronization and high speech quality.
• Score: 69.06657692891447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of Automatic Voice Over (AVO) is to generate speech in sync with a silent video given its text script. Recent AVO frameworks built upon text-to-speech synthesis (TTS) have shown impressive results. However, the current AVO learning objective of acoustic feature reconstruction brings in indirect supervision for inter-modal alignment learning, thus limiting the synchronization performance and synthetic speech quality. To this end, we propose a novel AVO method leveraging the learning objective of self-supervised discrete speech unit prediction, which not only provides more direct supervision for the alignment learning, but also alleviates the mismatch between the text-video context and acoustic features. Experimental results show that our proposed method achieves remarkable lip-speech synchronization and high speech quality by outperforming baselines in both objective and subjective evaluations. Code and speech samples are publicly available.

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