Learning task-specific subspaces via interventional post-training of speech foundation models

arXiv:2606.17967v1 Announce Type: new Abstract: Speech foundation models, pre-trained on large corpora of unlabelled speech data, produce general-purpose representations which are useful across tasks. However, these representations encode information about salient speech variables in a distributed manner, while downstream speech tasks rely on only some of this variability. In this work, we propose a post-training refinement approach using interventional contrastive learning. By leveraging an interventional dataset and multi-part contrastive loss, we learn a transformation from the entangled re

Source: arXiv cs.CL — read the full report at the original publisher.

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