论文标题
基于内部语言模型估计的语言模型融合跨域代码转换语音识别
Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition
论文作者
论文摘要
基于内部语言模型估计（ILME）语言模型（LM）融合已显示出明显改善的识别结果，而识别层内和跨域语音识别任务的常规浅融合。在本文中，我们试图将ILME方法应用于跨域代码转换语音识别（CSSR）工作。具体来说，我们的好奇心来自几个方面。首先，我们对基于ILME的LM融合对内域和跨域CSSR任务的有效有效。我们在不合并两个代码转换域的情况下对此进行验证。更重要的是，我们通过合并两个单语言数据集来训练端到端（E2E）语音识别模型，并观察提出的基于ILME的LM Fusion对CSSR的功效。来自东南亚和另一个中国大陆CS数据集的缝隙的实验结果证明了拟议的基于ILME的LM融合方法的有效性。
Internal Language Model Estimation (ILME) based language model (LM) fusion has been shown significantly improved recognition results over conventional shallow fusion in both intra-domain and cross-domain speech recognition tasks. In this paper, we attempt to apply our ILME method to cross-domain code-switching speech recognition (CSSR) work. Specifically, our curiosity comes from several aspects. First, we are curious about how effective the ILME-based LM fusion is for both intra-domain and cross-domain CSSR tasks. We verify this with or without merging two code-switching domains. More importantly, we train an end-to-end (E2E) speech recognition model by means of merging two monolingual data sets and observe the efficacy of the proposed ILME-based LM fusion for CSSR. Experimental results on SEAME that is from Southeast Asian and another Chinese Mainland CS data set demonstrate the effectiveness of the proposed ILME-based LM fusion method.
