XCB：一种有效的上下文偏置方法，用于语音识别中的跨语言短语偏置
XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition
AI总结：
本研究提出XCB模块，通过辅助语言偏置和语言特定损失增强预训练ASR模型，有效提升语码转换中次要语言短语的识别准确率，且无额外推理开销。
摘要：
上下文相关的ASR模型已被证明在预定义短语列表可用时能有效提高不常见短语的识别准确性。然而，这些模型在双语设置中往往表现不佳，而双语设置在语码转换语音识别中普遍存在。在本研究中，我们首次尝试通过引入跨语言上下文偏置（XCB）模块来解决这一挑战。具体而言，我们通过集成一个辅助语言偏置模块和一个补充的语言特定损失函数，增强了一个针对主导语言的预训练ASR模型，旨在提高对次要语言短语的识别能力。在我们内部语码转换数据集上进行的实验验证了该方法的有效性，表明即使在没有任何额外推理开销的情况下，对次要语言中偏置短语的识别也有显著改善。此外，我们提出的系统在应用于未见过的ASRU-2019测试集时，展现出高效性和泛化能力。
英文摘要：
Contextualized ASR models have been demonstrated to effectively improve the recognition accuracy of uncommon phrases when a predefined phrase list is available. However, these models often struggle with bilingual settings, which are prevalent in code-switching speech recognition. In this study, we make the initial attempt to address this challenge by introducing a Cross-lingual Contextual Biasing(XCB) module. Specifically, we augment a pre-trained ASR model for the dominant language by integrating an auxiliary language biasing module and a supplementary language-specific loss, aimed at enhancing the recognition of phrases in the secondary language. Experimental results conducted on our in-house code-switching dataset have validated the efficacy of our approach, demonstrating significant improvements in the recognition of biasing phrases in the secondary language, even without any additional inference overhead. Additionally, our proposed system exhibits both efficiency and generalization when is applied by the unseen ASRU-2019 test set.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
