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arXiv · 2506.02178

Cocktail-Party Audio-Visual Speech Recognition

Thai-Binh Nguyen·Ngoc-Quan Pham·Alexander Waibel
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Abstract

Audio-Visual Speech Recognition (AVSR) offers a robust solution for speech recognition in challenging environments, such as cocktail-party scenarios, where relying solely on audio proves insufficient. However, current AVSR models are often optimized for idealized scenarios with consistently active speakers, overlooking the complexities of real-world settings that include both speaking and silent facial segments. This study addresses this gap by introducing a novel audio-visual cocktail-party dataset designed to benchmark current AVSR systems and highlight the limitations of prior approaches in realistic noisy conditions. Additionally, we contribute a 1526-hour AVSR dataset comprising both talking-face and silent-face segments, enabling significant performance gains in cocktail-party environments. Our approach reduces WER by 67% relative to the state-of-the-art, reducing WER from 119% to 39.2% in extreme noise, without relying on explicit segmentation cues.

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Thai-Binh Nguyen, Ngoc-Quan Pham, Alexander Waibel. 2025-06-02. Cocktail-Party Audio-Visual Speech Recognition. https://arxiv.org/abs/2506.02178

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