提升语音增强语言模型在复杂场景下的实体识别准确率
COALA: Robust Contextualized Speech-augmented Language Modeling for ASR via Contrastive Regularizer and Biasing Score Estimation
- 通过对比正则化将语音表示映射到判别空间，量化音频与候选实体匹配度
- 在不同规模偏置列表下均优于基线，在多实体共现时表现更稳定
- 适合需要精准识别专业术语的语音识别场景，如医疗、法律领域
上下文偏置旨在将外部知识融入自动语音识别（ASR）系统，以准确识别领域特定实体。本文提出COALA（Contextualized ASR Leveraging Biasing Scoring），一种鲁棒框架，用于提升复杂多实体场景下语音增强语言模型（SLMs）的性能。考虑到SLMs固有的上下文窗口限制，从大规模偏置列表中识别相关目标实体至关重要。为此，COALA将SLM隐层表示映射至专用判别空间，量化音频片段与候选实体之间的匹配强度。此外，针对先前研究在处理多目标话语（多个罕见词共现）时出现的训练崩溃问题，本方法有效缓解。在LibriSpeech基准上的实验表明，COALA在不同规模偏置列表下均持续实现更优的上下文偏置性能。
原文摘要 · Abstract (English)
Contextual biasing seeks to integrate external knowledge into automatic speech recognition (ASR) systems to accurately recognize domain-specific entities. In this paper, we propose COALA (Contextualized ASR Leveraging Biasing Scoring), a robust framework designed to enhance speech-augmented language models (SLMs) in complex multi-entity scenarios. Considering the inherent context-window limitations of SLMs, identifying relevant target entities from a large-scale biasing list is crucial for effective recognition. To this end, COALA maps SLM latent representations into a specialized discriminative space to quantify the matching intensity between audio segments and candidate entities. Furthermore, we address the training collapse in prior study when handling multi-target utterances-where multiple rare words co-occur. Experimental results on the LibriSpeech benchmark demonstrate that COALA consistently achieves superior contextual biasing performance across various biasing list scales.
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