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arXiv · 2303.07005

Real-Time Audio-Visual End-to-End Speech Enhancement

Zirun Zhu·Hemin Yang·Min Tang·Ziyi Yang·Sefik Emre Eskimez·Huaming Wang
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Abstract

Audio-visual speech enhancement (AV-SE) methods utilize auxiliary visual cues to enhance speakers' voices. Therefore, technically they should be able to outperform the audio-only speech enhancement (SE) methods. However, there are few works in the literature on an AV-SE system that can work in real time on a CPU. In this paper, we propose a low-latency real-time audio-visual end-to-end enhancement (AV-E3Net) model based on the recently proposed end-to-end enhancement network (E3Net). Our main contribution includes two aspects: 1) We employ a dense connection module to solve the performance degradation caused by the deep model structure. This module significantly improves the model's performance on the AV-SE task. 2) We propose a multi-stage gating-and-summation (GS) fusion module to merge audio and visual cues. Our results show that the proposed model provides better perceptual quality and intelligibility than the baseline E3net model with a negligible computational cost increase.

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BibTeXRIS

Zirun Zhu, Hemin Yang, Min Tang, Ziyi Yang, Sefik Emre Eskimez, Huaming Wang. 2023-03-13. Real-Time Audio-Visual End-to-End Speech Enhancement. https://arxiv.org/abs/2303.07005

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