AGENTIC-SYSTEMSMarch 20, 20261 min read18 views

DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models

Researchers introduce DEAF, a benchmark designed to test whether audio AI models truly understand acoustic signals or merely rely on text-based inference, revealing a significant gap in genuine acoustic understanding.

Computer Science > Artificial Intelligence

Title:DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models

View PDF HTML (experimental)Abstract:Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuinely process acoustic signals or rely on text-based semantic inference. To systematically study this question, we introduce DEAF (Diagnostic Evaluation of Acoustic Faithfulness), a benchmark of over 2,700 conflict stimuli spanning three acoustic dimensions: emotional prosody, background sounds, and speaker identity. Then, we design a controlled multi-level evaluation framework that progressively increases textual influence, ranging from semantic conflicts in the content to misleading prompts and their combination, allowing us to disentangle content-driven bias from prompt-induced sycophancy. We further introduce diagnostic metrics to quantify model reliance on textual cues over acoustic signals. Our evaluation of seven Audio MLLMs reveals a consistent pattern of text dominance: models are sensitive to acoustic variations, yet predictions are predominantly driven by textual inputs, revealing a gap between high performance on standard speech benchmarks and genuine acoustic understanding.

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Source: arXiv cs.AI Recent

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