SGER: Experiments on Integrating Speech Recognition and Natural Language Processing

* 该项目致力于在语音理解系统中集成语音识别和自然语言处理组件。 目前大多数语音识别器使用词级隐马尔可夫模型（HMRM）结合n-gram语言模型。 通过使用电话作为基本识别单元，可以实现额外的灵活性。这项研究提出了一种新的技术称为SOHMM 随机观察隐马尔可夫模型-用于从候选电话中识别单词。 目前大多数口语系统使用概率语言模型来建模句法和语义模式。 该项目将集成一个基于SOHMM的语音识别器与约束依赖语法（CDG）解析器，它支持在统一的模块化框架中使用词汇，韵律，句法和语义知识源，以及使用特定领域的约束。 这项工作将细化模型的SOHMM-CDG口语处理系统，并解决问题的建设和修剪词图，这两个组件之间的接口。实验将证明集成系统的有效性识别和分析的TIMIT和资源管理语料库的话语。 句子的准确性应该比只使用统计语言模型的情况有显著的提高，特别是当使用特定领域的约束时。

*** This project addresses the integration of speech recognition and natural language processing components in a speech understanding system. Most current speech recognizers use word-level hidden Markov models (HMMs)in conjunction with n-gram language models. Additional flexibility can be achieved by using phones as the basic recognition unit. This research proposes a new technique called a SOHMM Stochastic Observation Hidden Markov Model - for recognizing words from phone candidates. Most current spoken language systems use probabilistic language models to model syntactic and semantic patterns. This project will integrate a SOHMM- based speech recognizer with a Constraint-Dependency Grammar (CDG) parser, which supports the use of lexical, prosodic, syntactic, and semantic knowledge sources in a uniform modular framework, as well as the use of domain- specific constraints. This work will refine the model for the SOHMM-CDG spoken language processing system and address the problem of building and pruning word graphs, which act as the interface between the two components. An experiment will be conducted to demonstrate the effectiveness of the integrated system for recognizing and parsing utterances in the TIMIT and Resource Management corpora. Sentence accuracy should be significantly improved over what is possible using only statistical language models, especially when domain-specific constraints are used ***.