用自适应推理链提升语音识别命名实体纠错能力
Retrieval-Augmented Self-Taught Reasoning Model with Adaptive Chain-of-Thought for ASR Named Entity Correction
- 通过重述模型与音似编辑距离检索候选实体
- 自适应推理深度，难题多推几步，简单题快速解决
- 在两个数据集上纠错率降低超17%，适合语音系统优化
端到端语音识别（ASR）系统常将领域特定短语如命名实体误识别，导致下游任务失败。近年来基于大语言模型（LLM）的命名实体纠错方法兴起，但尚未充分发挥其复杂推理能力。为此，我们提出一种新型检索增强生成框架用于纠正ASR中的命名实体错误。该方法包含两部分：(1) 使用重述语言模型（RLM）进行命名实体识别，并基于音似编辑距离检索候选；(2) 提出一种自教式推理模型（A-STAR），根据任务难度动态调整推理深度。在AISHELL-1和同音词数据集上的实验表明，相比强基线，该方法分别实现17.96%和34.42%的命名实体字符错误率相对下降。
原文摘要 · Abstract (English)
End-to-end automatic speech recognition (ASR) systems frequently misrecognize domain-specific phrases like named entities, which can cause catastrophic failures in downstream tasks. A new family of named entity correction methods based on large language models (LLMs) has recently emerged. However, these approaches have yet to fully exploit the sophisticated reasoning capabilities inherent to LLMs. To bridge this gap, we propose a novel retrieval-augmented generation framework for correcting named entity errors in ASR. Our approach consists of two key components: (1) a rephrasing language model (RLM) for named entity recognition, followed by candidate retrieval using a phonetic-level edit distance; and (2) a novel self-taught reasoning model with adaptive chain-of-thought (A-STAR) that dynamically adjusts the depth of its reasoning based on task difficulty. Experiments on the AISHELL-1 and Homophone datasets demonstrate the effectiveness of our method, which achieves relative reductions in the named entity character error rate of 17.96\% and 34.42\%, respectively, compared to a strong baseline.
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