Cross-Attention is Half Explanation in Speech-to-Text Models
Cross-attention is a core mechanism in encoder-decoder architectures,
widespread in many fields, including speech-to-text (S2T) processing. Its
scores have been repurposed for various downstream applications--such as
timestamp estimation and audio-text alignment--under the assumption that they
reflect the dependencies between input speech representation and the generated
text. While the explanatory nature of attention mechanisms has been widely
debated in the broader NLP literature, this assumption remains largely
unexplored within the speech domain. To address this gap, we assess the
explanatory power of cross-attention in S2T models by comparing its scores to
input saliency maps derived from feature attribution. Our analysis spans
monolingual and multilingual, single-task and multi-task models at multiple
scales, and shows that attention scores moderately to strongly align with
saliency-based explanations, particularly when aggregated across heads and
layers. However, it also shows that cross-attention captures only about 50% of
the input relevance and, in the best case, only partially reflects how the
decoder attends to the encoder's representations--accounting for just 52-75% of
the saliency. These findings uncover fundamental limitations in interpreting
cross-attention as an explanatory proxy, suggesting that it offers an
informative yet incomplete view of the factors driving predictions in S2T
models.
68d243a81ca7156988a8ee7e
Sara Papi
admin_assigned
2025-09-23T14:37:23.826Z
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Sara Papi
user
spapi
68d243a81ca7156988a8ee7f
Dennis Fucci
68d243a81ca7156988a8ee80
Marco Gaido
admin_assigned
2025-09-23T14:37:35.452Z
662e58f32b1b529a43c8b2f4
/avatars/bd26a7b26596882c6bc8a7375ff42cac.svg
Marco Gaido
user
mgaido91
68d243a81ca7156988a8ee81
Matteo Negri
admin_assigned
2025-09-23T14:37:42.444Z
66350219843f549fdac86347
/avatars/1ad7aa4e8e6d80c5d50bf4de502f11a4.svg
Matteo Negri
user
MNegri
68d243a81ca7156988a8ee82
Luisa Bentivogli
admin_assigned
2025-09-23T14:37:49.343Z
662eb48f5ade870b60db4380
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/ozHyP-GBIAEhR-l-TkBFb.jpeg
Luisa Bentivogli
user
lubentivogli
Cross-Attention is Half Explanation in Speech-to-Text Models
Cross-attention is a core mechanism in encoder-decoder architectures,
widespread in many fields, including speech-to-text (S2T) processing. Its
scores have been repurposed for various downstream applications--such as
timestamp estimation and audio-text alignment--under the assumption that they
reflect the dependencies between input speech representation and the generated
text. While the explanatory nature of attention mechanisms has been widely
debated in the broader NLP literature, this assumption remains largely
unexplored within the speech domain. To address this gap, we assess the
explanatory power of cross-attention in S2T models by comparing its scores to
input saliency maps derived from feature attribution. Our analysis spans
monolingual and multilingual, single-task and multi-task models at multiple
scales, and shows that attention scores moderately to strongly align with
saliency-based explanations, particularly when aggregated across heads and
layers. However, it also shows that cross-attention captures only about 50% of
the input relevance and, in the best case, only partially reflects how the
decoder attends to the encoder's representations--accounting for just 52-75% of
the saliency. These findings uncover fundamental limitations in interpreting
cross-attention as an explanatory proxy, suggesting that it offers an
informative yet incomplete view of the factors driving predictions in S2T
models.
Cross-attention in speech-to-text models aligns moderately with saliency-based explanations but captures only a portion of input relevance and decoder attention.
cross-attention encoder-decoder architectures speech-to-text timestamp estimation audio-text alignment feature attribution saliency maps monolingual multilingual single-task multi-task models