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New ASR Correction Method Uses Ontology Memory for Long Conversations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel ontology memory-augmented framework to improve automatic speech recognition (ASR) correction for long, interleaved text and speech conversations. This approach organizes interaction history into a dynamic ontology memory, storing entities, terminology, and semantic relations as retrievable nodes. Experiments using the newly constructed RAMC-Corr dataset demonstrate that this method significantly enhances correction accuracy compared to direct correction methods. AI

IMPACT This new ASR correction framework could improve the accuracy of speech-to-text systems in complex, long-form conversational contexts.

RANK_REASON The cluster contains a research paper detailing a new method for ASR correction.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Xinxin Li, Huiyao Chen, Meishan Zhang, Yunxin Li, Zulong Chen, Zhibo Ren, Xiaoqing Dong Baotian Hu, Min Zhang ·

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

    arXiv:2606.13464v1 Announce Type: cross 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 conv…

  2. arXiv cs.AI TIER_1 English(EN) · Min Zhang ·

    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 c…

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