Journal ArticleDOI
On the hidden Markov model and dynamic time warping for speech recognition — A unified view
TLDR
A unified theoretical view of the Dynamic Time Warping (DTW) and the Hidden Markov Model (HMM) techniques for speech recognition problems is given and offers insights into the effectiveness of the probabilistic models in speech recognition applications.Citations
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
Journal ArticleDOI
Hidden Markov models for speech recognition
TL;DR: The role of statistical methods in this powerful technology as applied to speech recognition is addressed and 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 are discussed.
Book
Connectionist Speech Recognition: A Hybrid Approach
Hervé Bourlard,Nelson Morgan +1 more
TL;DR: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance.
Book
Application of Hidden Markov Models in Speech Recognition
Mark J. F. Gales,Steve Young +1 more
TL;DR: The aim of this review is first to present the core architecture of a HMM-based LVCSR system and then to describe the various refinements which are needed to achieve state-of-the-art performance.
References
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Journal ArticleDOI
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
Journal ArticleDOI
Linear prediction: A tutorial review
TL;DR: This paper gives an exposition of linear prediction in the analysis of discrete signals as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal.
Journal ArticleDOI
Minimum prediction residual principle applied to speech recognition
TL;DR: A computer system is described in which isolated words, spoken by a designated talker, are recognized through calculation of a minimum prediction residual through optimally registering the reference LPC onto the input autocorrelation coefficients using the dynamic programming algorithm.
Journal ArticleDOI
An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition
TL;DR: This paper presents several of the salient theoretical and practical issues associated with modeling a speech signal as a probabilistic function of a (hidden) Markov chain, and focuses on a particular class of Markov models, which are especially appropriate for isolated word recognition.