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Paper Argues ASR Transcripts Are a Flawed Yardstick for Audio-LLM Tasks
A research paper examines how speech and audio LLMs are typically evaluated, namely by testing whether a waveform prompt outperforms an automatic speech recognition transcript. The authors argue that for closed-set, known tasks this setup mixes together two distinct things: whether the model actually used acoustic information and whether it simply needed the task spelled out. They propose auditing generative audio calls as a way to separate those factors.