Cross-attention in speech-to-text models correlates with saliency-based explanations (Pearson r roughly 0.49-0.75 in the best aggregations) but explains only a minority of the variance, so it should complement, not replace, attribution methods.
MOSEL : 950,000 hours of speech data for open-source speech foundation model training on EU languages
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Cross-Attention is Half Explanation in Speech-to-Text Models
Cross-attention in speech-to-text models correlates with saliency-based explanations (Pearson r roughly 0.49-0.75 in the best aggregations) but explains only a minority of the variance, so it should complement, not replace, attribution methods.