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

TL;DR

This paper introduces a new automatic voice-over method that uses self-supervised discrete speech units to improve lip-speech synchronization and speech quality, outperforming existing approaches.

Contribution

It proposes a novel AVO framework leveraging self-supervised discrete speech units for direct alignment supervision, enhancing synchronization and speech quality.

Findings

Achieves superior lip-speech synchronization

Improves speech quality over baselines

Outperforms existing methods in evaluations

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.