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Using semantic analysis to improve speech recognition performance

机译：使用语义分析提高语音识别性能

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摘要

Although syntactic structure has been used in recent work in language modeling, there has not been much effort in using semantic analysis for language models. In this study, we propose three new language modeling techniques that use semantic analysis for spoken dialog systems. We call these methods concept sequence modeling, two-level semantic-lexical modeling, and joint semantic-lexical modeling. These models combine lexical information with varying amounts of semantic information, using annotation supplied by either a shallow semantic parser or full hierarchical parser. These models also differ in how the lexical and semantic information is combined, ranging from simple interpolation to tight integration using maximum entropy modeling. We obtain improvements in recognition accuracy over word and class N-gram language models in three different task domains. Interpolation of the proposed models with class N-gram language models provides additional improvement in the air travel reservation domain. We show that as we increase the semantic information utilized and as we increase the tightness of integration between lexical and semantic items, we obtain improved performance when interpolating with class language models, indicating that the two types of models become more complementary in nature.
机译：尽管语法结构已在语言建模的最新工作中使用，但在将语义分析用于语言模型方面并没有付出太多努力。在这项研究中，我们提出了三种新的语言建模技术，这些技术将语义分析用于语音对话系统。我们将这些方法称为概念序列建模，两级语义-词法建模和联合语义-词法建模。这些模型使用浅层语义解析器或完整层次解析器提供的注释，将词汇信息与数量不等的语义信息结合在一起。这些模型的词汇和语义信息的组合方式也有所不同，从简单的插值到使用最大熵建模的紧密集成，不一而足。我们在三个不同任务域中的单词和类N-gram语言模型上获得了更高的识别准确性。用类N-gram语言模型对提议的模型进行插值，可以在航空旅行预订领域提供额外的改进。我们显示出，随着我们增加所利用的语义信息以及随着词汇和语义项之间的紧密集成，在使用类语言模型进行插值时，我们获得了改进的性能，表明这两种类型的模型在本质上变得更加互补。

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