SVHalluc: Benchmarking Speech-Vision Hallucination in Audio-Visual Large Language Models

2026-05-31

AI Summary

This work addresses the challenge that existing audio-visual large language models struggle to accurately align spoken content with visual signals, often generating semantic or temporal hallucinations. To this end, the paper introduces SVHalluc, the first benchmark specifically designed to evaluate speech–vision hallucination, systematically assessing cross-modal understanding along two dimensions: semantic consistency and temporal alignment. The benchmark employs a multi-task framework and includes comprehensive experiments covering both open-source and proprietary state-of-the-art models. Results reveal that current open-source models perform near random chance, while Gemini 2.5 Pro demonstrates significant superiority, highlighting a critical gap in existing approaches for speech–vision alignment and establishing a much-needed evaluation standard in this emerging domain.

Abstract

Despite the success of audio-visual large-language models (LLMs), they can produce plausible but ungrounded outputs, termed hallucination. Existing benchmarks focus on environmental sounds (e.g., dog barking) to indicate event occurrence. In contrast, human speech carries fundamentally different, rich semantics and temporal structures, yet it remains unexplored whether current models can accurately align speech content with corresponding visual signals. In this work, we show that speech content can induce hallucinations in audio-visual LLMs. To systematically study this, we introduce SVHalluc, the first comprehensive benchmark for evaluating speech-vision hallucination in audio-visual LLMs. Our benchmark diagnoses speech-vision hallucinations from two critical and complementary aspects: semantic and temporal. Experimental results demonstrate that state-of-the-art open-source audio-visual LLMs struggle with aligning speech content with corresponding visual signals, with a near-random accuracy on multiple tasks. In contrast, Gemini 2.5 Pro significantly outperforms the open-source models. Our analysis suggests that their failures stem from limited ability in cross-modality understanding, despite strong performance in single-modality perception. Our work uncovers a new and fundamental limitation of current audio-visual LLMs and highlights the need for speech-grounded video comprehension. Project page: https://chenshuang-zhang.github.io/projects/svhalluc/.

Problem

• speech-vision hallucination
• audio-visual LLMs
• cross-modality alignment
• temporal alignment
• semantic grounding

Innovation

• speech-vision hallucination
• audio-visual LLMs
• cross-modality alignment
• temporal grounding
• multimodal benchmark