A tutorial on hidden Markov models and selected applications in speech recognition

  title={A tutorial on hidden Markov models and selected applications in speech recognition},
  author={Lawrence R. Rabiner},
  journal={Proc. IEEE},
The fabric comprises a novel type of netting which will have particular utility in screening out mosquitoes and like insects and pests. The fabric is defined of voids having depth as well as width and length. The fabric is usable as a material from which to form clothing for wear, or bed coverings, or sleeping bags, etc., besides use simply as a netting. 

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HMM classifiers have been extensively used in continuous speech recognition (CSR) for many years and are used to perform OCR in this work.

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    2011 IEEE International Conference on Cloud Computing and Intelligence Systems
  • 2011
A domestic speech recognizer, which can be used at home to do some simple tasks, such as turning on/off the light, opening/closing the doors and turning up/down air conditioners' temperature according to voice commands is proposed.

Classifying bags of keypoints using HMMs

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These Ecole polytechnique federale de Lausanne EPFL students have taught at this university since the year 1860, and the curriculum has changed little in the intervening years.

Gesture recognition based on subspace method and hidden Markov model

  • Y. IwaiT. HataM. Yachida
  • Computer Science
    Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97
  • 1997
This paper proposes a method to recognize human gestures from an image sequence based on hidden Markov model and subspace method that is robust for variety in the background of the scene and it doesn't require the users to wear a sensor or a marker.



Global connected digit recognition using Baum-Welch algorithm

  • C. Wellekens
  • Computer Science
    ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing
  • 1986
A connected speech recognition method based on the Baum forward backward algorithm is presented. The segmentation of the test sentence uses the probability that an acoustic vector lays at the

Vector quantization and Markov source models applied to speech recognition

  • R. Billi
  • Physics, Computer Science
  • 1982
An isolated word recognizer based on vector quantization at the acoustic level and on stochastic modeling at the phonetic level is described and results obtained are encouraging and suggest that further optimization is possible.

Recognition of isolated digits using hidden Markov models with continuous mixture densities

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Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition

  • M. RussellR. Moore
  • Computer Science
    ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing
  • 1985
Results have been presented which show that these semi-Markov models provide an appropriate framework for modelling durational structure and can lead to significant improvements in recognition accuracy.

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In this paper we present an approach to speaker-independent, isolated word recognition in which the well-known techniques of vector quantization and hidden Markov modeling are combined with a linear

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These investigations to the recognition of isolated words from a medium size vocabulary, (129 words), as used in the Bell Laboratories airline reservation and information system, find that recognition accuracy is indeed a function of the HMM parameter and that a vector quantizer which uses energy information gives better performance.

An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition

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.

A Maximum Likelihood Approach to Continuous Speech Recognition

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Linear predictive hidden Markov models and the speech signal

A method for modelling time series is presented and then applied to the analysis of the speech signal, resulting in a theorem that provides a computationally efficient iterative scheme to improve the model.