Automatic Speech Recognition Based on Syllable-length Acoustic Models

具有比传统电话或子电话更长的时间基础的单元可以为为识别自然发声的话语而定制的ASR系统提供更好的结构基础。在这个项目中，正在探索用于识别会话语音的音节长度的声学模型。该项目需要：确定从大约250毫秒语音间隔的能量轨迹中得出的声学特征；对这些音节长度区域进行统计建模；制定一种解码方案，以便将这些声学模型的输出与更传统的音长和子音长模型结合起来；将音节、音素和子音节特征嵌入语言的多层表示中，以便在广泛的声学环境和说话条件下进行稳健的识别。一个完整的ASR系统正在开发中，它将纳入这项研究的结果，并将在流利的演讲中进行评估。识别系统的成功结果有可能改进实际的ASR系统，这些系统必须处理自发话语的解码。此外，在声学、统计和词汇层面对较长时间结构的分析将提高我们对会话言语结构的基本知识。

Units with a longer time basis than the traditional phones or sub- phones may provide a better structural basis for ASR systems customized for recognition of naturally spoken discourse. In this project, syllabic-length acoustic models are being explored for the recognition of conversational speech. The project entails: definition of the acoustic features derived from energy trajectories spanning ca. 250-ms intervals of speech; statistical modeling of these syllable-length regions; development of a decoding scheme designed to combine the outputs of these acoustic models with the more traditional phone- and sub-phone-length models; and embedding the syllable, phone and sub-phone features into a multi-tiered representation of language designed for robust recognition under a wide range of acoustic-environmental and speaking conditions. A complete ASR system is being developed that will incorporate the results of this research, and will be evaluated on fluent speech. Successful results with the recognition system have the potential to improve practical ASR systems that must deal with the decoding of spontaneous discourse. Additionally, the analysis of longer-time structure at the acoustic, statistical, and lexical levels should improve our basic knowledge about the structure of conversation speech.