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We introduce target-speaker unlearning ASR (TSU-ASR) task in a fully end-to-end framework for multi-speaker ASR and diarization. Given a multi-speaker utterance and a set of opt-out speakers who do not wish to have their speech transcribed, the task requires an ASR system to transcribe all speakers except the opt-out o...
How unlearning effects propagate from the forget set to same-domain and distant-domain knowledge not after but before unlearning is measured to find a consistent decay pattern: collateral damage is strongest near the forget set, weakens with semantic distance, but does not disappear at domain boundaries.
It is established that a localized internal activation signal tracks changes in recommendations, and it is shown that removing the internal signal associated with one cue can suppress its influence, often more effectively than asking the model to ignore demographics via prompting.
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