Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations

arXiv:2606.13464v1 Announce Type: new Abstract: Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and speech become increasingly interleaved in long interactions, ASR correction requires conversation-level contextual evidence. Existing ASR correction methods often rely on the current hypothesis or concatenate raw dialogue history. In such contexts, sparse correction evidence can be difficult to locate amid redundancy and noise. Addressing these challenges, we propose an ontology memory-augmented ASR correcti

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

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