AI & ML
Measuring Local Acoustic Contribution in Speech LLMs via Word-Span Counterfactual Interventions
arXiv cs.SD0 views
arXiv:2610.33077v1 Announce Type: new Abstract: Correct transcription does not establish that a word depended on its local audio. We introduce the Acoustic Evidence Contribution (AEC), a word-level counterfactual measure of acoustic grounding. It is obtained from the change in reference-token log likelihood when the word's aligned acoustic span is replaced. We combine decoding outcomes on unedited audio with thresholded AEC in an outcome-contribution matrix, where correct targets are categorized according to their local acoustic contribution as high or low. Depending on the system, 33-49% of correct single-word targets fall below the twofold contribution threshold when averaged across corpora, despite no evident rank association with WER. Retention of reference words after span removal decreases monotonically across AEC quintiles, from 76-83% in the lowest quintile to approximately 1% in the highest. AEC therefore separates transcription correctness from dependence on the aligned local acoustic span while remaining an oracle-aligned diagnostic method.
Source:https://arxiv.org/abs/2610.33077PreviousDo bioacoustic foundation models hear through the noise ? SNR and generalization in underwater detectionNextMarineSet: Large-Scale Underwater Acoustic Data Curation and evaluation
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