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

@article{Rabiner1989ATO,
  title={A tutorial on hidden Markov models and selected applications in speech recognition},
  author={Lawrence R. Rabiner},
  journal={Proc. IEEE},
  year={1989},
  volume={77},
  pages={257-286}
}
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. 
A Neural Predictive Hidden Markov Model Architecture for Speech and Speaker Recognition
TLDR
This research presents a means through which people can talk to computers in much the same way as they carry on conversations with fellow humans through automatic speech recognition.
Investigating hidden Markov models' capabilities in 2D shape classification
TLDR
The results of tests show that the proposed system is able to accurately classify objects that were translated, rotated, occluded, or deformed by shearing, also in the presence of noise.
A Soft-Core for Pattern Recognition
TLDR
The design of a HMM soft-core for the recognition stage in an independent speaker isolated word recognition system is introduced, which may be useful in many applications.
Optical Character Recognition using Hidden Markov Models
  • Computer Science
  • 2016
TLDR
HMM classifiers have been extensively used in continuous speech recognition (CSR) for many years and are used to perform OCR in this work.
Model Selection of Combined Neural Nets for Speech Recognition
The problem of finding criteria through which a model will be chosen to match a problem and available data and give optimal future performance is a crucial issue in practical applications, not to be
A domestic speech recognition based on Hidden Markov Model
  • Jun Tao, Xiaoxiao Jiang
  • Computer Science
    2011 IEEE International Conference on Cloud Computing and Intelligence Systems
  • 2011
TLDR
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
TLDR
A Hidden Markov Models is used to classify bags of SURF keypoints descriptors of a given class and a prospective of expanding this application to include the detection and classification of moving objects in a video stream using optical flow and Self Organizing Maps (SOM).
Hidden Markov models and artificial neural networks for speech and speaker recognition
TLDR
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. Iwai, T. Hata, M. 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
TLDR
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.
A Method to Combine HMM and BPNN on Speech Recognition
  • Wu-Feng, Chai-Yi
  • Computer Science, Engineering
    2007 International Conference on Machine Learning and Cybernetics
  • 2007
Applied the concept of phonic state in Hidden Markov Model to construct the input matrix in BP Neural Network as modeling and recognition, which can decrease their dimension (almost to 1/3 - 1/5)
...
...

References

SHOWING 1-10 OF 98 REFERENCES
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
    ICASSP
  • 1982
TLDR
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
In this paper we extend previous work on isolated-word recognition based on hidden Markov models by replacing the discrete symbol representation of the speech signal with a continuous Gaussian
Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition
  • M. Russell, R. Moore
  • Computer Science
    ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing
  • 1985
TLDR
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.
On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition
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
On the use of hidden Markov models for speaker‐independent recognition of isolated words from a medium size vocabulary
TLDR
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
TLDR
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
TLDR
This paper describes a number of statistical models for use in speech recognition, with special attention to determining the parameters for such models from sparse data, and describes two decoding methods appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks.
Linear predictive hidden Markov models and the speech signal
TLDR
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.
...
...