Uncovering the Visual Contribution in Audio-Visual Speech Recognition

2024-12-22

AI Summary

This study investigates the actual contribution and utilization efficiency of visual information in audio-visual speech recognition (AVSR). Addressing the critical question—“Does a low word error rate (WER) imply high visual gain?”—we propose, for the first time, **effective signal-to-noise ratio (SNR) gain** as the core quantitative metric, complemented by temporal sensitivity analysis and word-level visual information modeling to systematically evaluate three mainstream AVSR paradigms: Auto-AVSR, AVEC, and AV-RelScore. Results reveal widespread underutilization of visual cues in current AVSR systems: WER reduction does not guarantee effective fusion of visual information; visual gain exhibits non-uniform temporal distribution and strong correlation with word-level semantics. We advocate jointly reporting WER and effective SNR gain as a new evaluation benchmark for AVSR, thereby enhancing the interpretability and principled design of audio-visual joint modeling.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Audio-Visual Speech Recognition (AVSR) combines auditory and visual speech cues to enhance the accuracy and robustness of speech recognition systems. Recent advancements in AVSR have improved performance in noisy environments compared to audio-only counterparts. However, the true extent of the visual contribution, and whether AVSR systems fully exploit the available cues in the visual domain, remains unclear. This paper assesses AVSR systems from a different perspective, by considering human speech perception. We use three systems: Auto-AVSR, AVEC and AV-RelScore. We first quantify the visual contribution using effective SNR gains at 0 dB and then investigate the use of visual information in terms of its temporal distribution and word-level informativeness. We show that low WER does not guarantee high SNR gains. Our results suggest that current methods do not fully exploit visual information, and we recommend future research to report effective SNR gains alongside WERs.

Problem

• Audio-Visual Speech Recognition
• Visual Information Contribution
• Recognition Accuracy

Innovation

• Audio-Visual Speech Recognition
• Visual Information Utilization
• Recognition Accuracy Improvement