Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
For researchers working on ASR correction in conversational AI, this work introduces a novel memory structure to handle long-range context, though the gains are incremental over existing methods.
The paper addresses ASR correction in long text-speech interleaved conversations, where traditional methods fail due to sparse and noisy context. They propose an ontology memory-augmented framework that organizes interaction history into a dynamic ontology for context-grounded correction, achieving improvements in 9 out of 10 backbone-setting combinations on the RAMC-Corr dataset.
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 correction framework for long text-speech interleaved conversations. The framework organizes preceding interaction history into a dynamically updatable ontology memory, where entities, terminology, surface variants, potential ASR confusions, and semantic relations are stored as retrievable nodes for context-grounded correction. To evaluate this setting, we construct RAMC-Corr, a dataset derived from MAGIC-RAMC for long-range ASR correction with grounded context. Experiments on RAMC-Corr show that our method improves over direct correction in 9 out of 10 paired backbone-setting combinations and encourages more selective and evidence-grounded corrections for context-dependent ASR errors.