Statistical Speech Recognition
This chapter describes the state-of-the-art technology for statistical ASR based on the pattern recognition paradigm. The most widely used core technology is the hidden Markov model (HMM). This is basically a Markov chain that characterizes a speech signal in a mathematically tractable way. Section 2.1 provides an overview of pattern recognition. In Section 2.2, we review the theory of Markov chains and the general form of an HMM, including three practical problems in using HMMs. In Section 2.3, we describe in detail the pattern recognition task for HMM-based ASR systems, starting from feature extraction, which processes the speech signal into a set of feature patterns, up through the search algorithm, which maps those features into the most probable strings of words. We also explain language modeling, the pronunciation dictionary, and acoustic modeling, including phone-unit-dependent models, speech observation density, and various approaches to parameter trying.
KeywordsHide Markov Model Speech Signal Vocal Tract Acoustic Model Viterbi Algorithm
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