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arXiv CS.AI·3d ago·5 min read
DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models
TL;DR
DEAF, a new benchmark for evaluating Acoustic Faithfulness in Audio Multimodal Large Language Models (Audio MLLMs), reveals that these models primarily rely on textual inputs rather than genuine acoustic understanding. The study highlights a significant gap between high performance on speech benchmarks and actual processing of acoustic signals.
✦ Why It Matters
Evaluate your Audio MLLMs using DEAF to ensure they genuinely understand acoustic signals.
Key Takeaways
- Utilize DEAF to benchmark your audio models' acoustic understanding.
- Assess the influence of textual prompts on model predictions.
- Explore the three acoustic dimensions: emotional prosody, background sounds, speaker identity.
- Implement diagnostic metrics to quantify reliance on textual versus acoustic cues.
Full Summary
Recent advancements in Audio Multimodal Large Language Models (Audio MLLMs) have shown impressive results on speech tasks, but their true understanding of acoustic signals is questionable. To investigate this, DEAF (Diagnostic Evaluation of Acoustic Faithfulness) was developed, comprising over 2,700 conflict stimuli across three acoustic dimensions: emotional prosody, background sounds, and speaker identity.
A multi-level evaluation framework was designed to assess the models' reliance on textual versus acoustic cues, introducing diagnostic metrics to quantify this reliance. Evaluating seven Audio MLLMs revealed a consistent trend where, despite sensitivity to acoustic variations, predictions were predominantly influenced by textual inputs.
This indicates a disconnect between high performance on standard benchmarks and actual acoustic comprehension, suggesting that models may not be processing audio signals as effectively as previously thought.