Real-Time Audio-Visual Speech Enhancement Using Pre-trained Visual Representations
Abstract
Speech enhancement in audio-only settings remains challenging, particularly in the presence of interfering speakers. This paper presents a simple yet effective real-time audio-visual speech enhancement (AVSE) system, RAVEN, which isolates and enhances the on-screen target speaker while suppressing interfering speakers and background noise. We investigate how visual embeddings learned from audio-visual speech recognition (AVSR) and active speaker detection (ASD) contribute to AVSE across different SNR conditions and numbers of interfering speakers. Our results show concatenating embeddings from AVSR and ASD models provides the greatest improvement in low-SNR, multi-speaker environments, while AVSR embeddings alone perform best in noise-only scenarios. In addition, we develop a real-time streaming system that operates on a computer CPU and we provide a video demonstration and code repository. To our knowledge, this is the first open-source implementation of a real-time AVSE system.
Cite
@article{arxiv.2507.21448,
title = {Real-Time Audio-Visual Speech Enhancement Using Pre-trained Visual Representations},
author = {T. Aleksandra Ma and Sile Yin and Li-Chia Yang and Shuo Zhang},
journal= {arXiv preprint arXiv:2507.21448},
year = {2025}
}
Comments
Accepted into Interspeech 2025; corrected author name typo