RI: Small: Modeling Coarticulation for Automatic Speech Recognition

本项目致力于将文本到语音合成(TTS)中使用的模型应用到自动语音识别(ASR)任务中。ASR中处理因音位上下文或协同发音而产生的可变性的标准方法。需要大量的训练数据，并且对训练和测试条件之间的差异很敏感。尽管有效地使用了随机模型，但目前的ASR系统往往不能充分解释在语音中观察到的很大程度的可变性。在许多情况下，这种变化不是由于随机因素，而是由于语音信号的可预测变化。目前对这些因素进行建模是为了通过TTS生成语音，但它们还没有建模以识别语音，这主要是因为非本地依赖。将TTS中使用的异步内插模型(AIM)应用到语音识别任务中，将语音信号分解为目标向量和加权轨迹，然后搜索加权轨迹和随机目标向量模型，以获得与输入信号最高概率的匹配。本研究的目的是提高ASR对音位和词汇语境变化的稳健性。这一改进将增加ASR技术在通过电话、教育软件以及对有视觉、听觉或语音产生困难的个人的普遍访问中的自动化信息访问中的使用。更有效的协同发音模型可能会增加我们对人类言语感知和言语产出的理解。该项目的成果通过技术文件和CSLU工具包软件包进行传播。

This project focuses on applying a model used in text-to-speech synthesis (TTS) to the task of automatic speech recognition (ASR). The standard method in ASR for addressing variability due to phonemic context, or ?coarticulation,? requires a large amount of training data and is sensitive to differences between training and testing conditions. Despite the effective use of stochastic models, current ASR systems are often unable to sufficiently account for the large degree of variability observed in speech. In many cases, this variability is not due to random factors, but is due to predictable changes in the speech signal. These factors are currently modeled in order to generate speech via TTS, but they are not yet modeled in order to recognize speech, largely because of non-local dependencies. We apply the Asynchronous Interpolation Model (AIM) used in TTS to the task of speech recognition, by decomposing the speech signal into target vectors and weight trajectories, and then searching weight-trajectory and stochastic target-vector models for the highest-probability match to the input signal. The goal of this research is improve the robustness of ASR to variability that is due to phonemic and lexical context. This improvement will increase the use of ASR technology in automated information access by telephone, educational software, and universal access for individuals with visual, auditory, or speech-production challenges. More effective models of coarticulation may increase our understanding of both human speech perception and speech production. Results from this project are disseminated through technical papers and the CSLU Toolkit software package.