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

Proposes an ontology memory-augmented framework for long text-speech interleaved conversations.

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

By Xinxin Li, Huiyao Chen, Meishan Zhang, Yunxin Li, Zulong Chen, Zhibo Ren, Xiaoqing Dong, Baotian HuarXiv
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The paper proposes a framework that organizes preceding interaction history into a dynamically updatable ontology memory to improve automatic speech recognition correction. The framework stores entities, terminology, and semantic relations as retrievable nodes for context-grounded correction.

Experiments show improved results over direct correction in most paired backbone-setting combinations.

Abstract

The paper proposes a framework that organizes preceding interaction history into a dynamically updatable ontology memory to improve automatic speech recognition correction. The framework stores entities, terminology, and semantic relations as retrievable nodes for context-grounded correction. Experiments show improved results over direct correction in most paired backbone-setting combinations.

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ontology memory-augmented asr correctionlong text-speech interleaved conversationsautomatic speech recognitioncontext-grounded correctionknowledge graph-based frameworkKnowledge GraphsStructured ContentContent EngineeringAI Agents
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