RI: Small: Exploratory Data Analysis for Speech Recognition

隐马尔可夫模型（HMM）已经成功地应用于自动语音识别超过35年，即使一个关键的HMM假设-帧的统计独立性-显然是违反了语音数据。事实上，这种数据/模型的不匹配激发了许多尝试，用能够更好地考虑帧的统计依赖性的替代模型来修改或替换HALCOST。这项工作的科学目标是发现语音数据中统计依赖的可预测区域，并量化它们对基于HMM的识别精度的影响。与以往的统计依赖性研究不同，本研究使用HMM通过探索性数据分析（EDA）来探索其与数据的偏离。该方法是首先分析数据及其与模型的拟合，搜索可预测的统计依赖区域-模型/数据不匹配。EDA再次被用来开发简单的模型的可预测的不匹配识别精度的影响。 这种分析的一个关键部分是开发和使用图形工具来可视化统计依赖性、识别错误及其关系。本文的研究结果将为隐马尔可夫模型推广的设计提供重要的线索。分析方法是统计学领域的核心，但很少用于语音识别研究。从事这个项目的研究生将学习它的实用性以及如何在其他问题上使用它。所开发软件的开放源码版本将可供免费下载。

Hidden Markov models (HMMs) have been successfully applied to automatic speech recognition for more than 35 years even though a key HMM assumption - the statistical independence of frames - is obviously violated by speech data. In fact, this data/model mismatch has inspired many attempts to modify or replace HMMs with alternative models that are better able to take into account the statistical dependence of frames. The scientific goal of this work is to discover predictable regions of statistical dependence in speech data and quantify their effect on HMM-based recognition accuracy. In contrast to previous studies of statistical dependency, this research uses the HMM to explore its departure from the data via exploratory data analysis (EDA). The methodology is to first analyze the data and its fit to the model, searching for regions of predictable statistical dependence - model/data mismatch. EDA is used again to develop simple models of the effect of the predictable mismatch on recognition accuracy. A key piece of this analysis is the development and use of graphical tools to visualize the statistical dependency, the recognition errors, and their relationship. The results of this research will provide important clues for the design of HMM generalizations. The analysis methodology is central to the field of statistics, but is rarely used in speech recognition research. Graduate students working on this project will learn its utility and how to use it on other problems. Open source versions of the software developed will be made available for free downloading.