Hidden Markov Models for Speech Recognition

Abstract

The use of hidden Markov models for speech recognition has become predominant in the last several years, as evidenced by the number of published papers and talks at major speech conferences. The reasons this method has become so popular are the inherent statistical (mathematically precise) framework: the ease and availability of training algorithms for estimating the parameters of the models from finite training sets of speech data; the flexibility of the resulting recognition system in which one can easily change the size, type, or architecture of the models to suit particular words, sounds. and so forth; and the ease of implementation of the overall recognition system. In this expository article, we address the role of statistical methods in this powerful technology as applied to speech recognition and discuss a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations.

Bayesian event detection for sport games with hidden Markov model

“…• Model extensions: In many cases of sequential data modeling, model extensions with generalized HMMs, also known as hidden semi-Markov Models, can improve the modeling performance, and they have been successfully applied to several problems (e.g., [27,28]). Model extensions, including such a generalized HMM-based approach, are expected to be effective for event detection problems.…”

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“…The typical ASR system adopts a statistical approach based on hidden Markov models (HMMs) [22], and is composed by five main blocks: front end, phonetic dictionary, acoustic model, language model and decoder, as indicated in Figure 1. The two main ASR applications are command and control and dictation [18].…”

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“…The underlying assumption of the HMM is that the speech signal can be well characterized as a parametric random access, and the parameters of the stochastic process can be predicted in a precise, and well-defined manner. The HMM method provides a natural and highly reliable way of recognizing speech for a wide range of applications [12], [13].…”