做语音识别或对话系统的团队，这个框架解决了长对话中ASR纠错缺乏上下文的问题，直接提升纠错准确率，值得在长交互场景中尝试。
传统ASR纠错仅关注孤立语句或短上下文，但在长文本与语音交错的对话中，需要对话级上下文证据。现有方法依赖当前假设或拼接原始对话历史，难以在冗余噪声中定位稀疏纠错证据。本文提出本体记忆增强的ASR纠错框架，将交互历史组织为动态更新的本体记忆，存储实体、术语、表面变体、潜在ASR混淆及语义关系作为可检索节点。基于MAGIC-RAMC构建的RAMC-Corr数据集实验显示，该方法在10个配对骨干设置组合中9个优于直接纠错，促进更选择性、基于证据的上下文相关ASR错误纠正。
Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
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.