用词频语言模型和实体列表提升语音识别对生僻词的准确率
Unifying Global and Near-Context Biasing in a Single Trie Pass
- 结合命名实体列表与词级n-gram语言模型实现上下文感知的关键词偏向
- 在四种语言上使实体识别错误率降低32%，整体错误率减少12%
- 仅需一次前缀树遍历，适合部署在资源受限的实时语音系统
尽管端到端语音识别（ASR）模型已取得显著进展，但在识别罕见词（包括命名实体）及仅用文本数据适应新领域方面仍存在挑战。本文提出一种新颖且实用的方法，将命名实体偏置列表与词级n-gram语言模型相结合，兼顾简单性与有效性，在不损害或反而提升整体性能的前提下，显著改善实体识别效果。该方法被高效集成至基于变换器的ASR系统中，实现上下文自适应，计算开销几乎可忽略。我们在三个涵盖四种语言的数据集上进行了实验，并与当前最优的偏置策略进行对比。结果表明，该组合方案可使实体识别错误率相对降低最高达32%，整体词错误率（WER）相对减少最多12%。
原文摘要 · Abstract (English)
Despite the success of end-to-end automatic speech recognition (ASR) models, challenges persist in recognizing rare, out-of-vocabulary words - including named entities (NE) - and in adapting to new domains using only text data. This work presents a practical approach to address these challenges through an unexplored combination of an NE bias list and a word-level n-gram language model (LM). This solution balances simplicity and effectiveness, improving entities' recognition while maintaining or even enhancing overall ASR performance. We efficiently integrate this enriched biasing method into a transducer-based ASR system, enabling context adaptation with almost no computational overhead. We present our results on three datasets spanning four languages and compare them to state-of-the-art biasing strategies. We demonstrate that the proposed combination of keyword biasing and n-gram LM improves entity recognition by up to 32% relative and reduces overall WER by up to a 12% relative.
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