RI: Small: Exploratory Data Analysis for Speech Recognition

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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.