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Contextual vector quantization for speech recognition with discrete hidden Markov model
Proceedings of ICSIPNN '94. International Conference on Speech, Image Processing and Neural Networks
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Discrete Hidden Markov Model is used to increase the speed of speech recognition. ...
In recent years, Speech Recognition has the great development in the automation industry. ...
Discrete Hidden Markov Model: Discrete Hidden Markov Model is used to accelerate the speed of Speech Recognition. A Codebook is to be first generated for the feature vectors. ...
doi:10.1109/sipnn.1994.344816
fatcat:eft6ohhaeje2nfdwaa7rsdorum
An Efficient Speech Recognition System
2013
Computer Science & Engineering An International Journal
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This paper describes the development of an efficient speech recognition system using different techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and Hidden Markov ...
Then HMM is used on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values for the spoken word. ...
M.S.Indira for her support. Our special thanks to Prof. Dilip.K.Sen, HOD of CSE for his valuable suggestions from time to time. ...
doi:10.5121/cseij.2013.3403
fatcat:5w7tjuttenhknihinjaq6b546e
Connectionist Approaches to the Use of Markov Models for Speech Recognition
1990
Neural Information Processing Systems
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Previous work has shown the ability of Multilayer Perceptrons (MLPs) to estimate emission probabilities for Hidden Markov Models (HMMs). ...
Results support the previously reported utility of MLP probability estimation for continuous speech recognition. ...
For a sequence of acoustic vectors X = {Xl, ... , XN} and a Markov model M, P(XIM) cannot simply be obtained by DP recurrences (which are only valid for first order Markov models) using the contextual ...
dblp:conf/nips/BourlardMW90
fatcat:pmqntuuiancmna4pjedkufdosu
Links between Markov models and multilayer perceptrons
1990
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Hidden Markov models are widely used for automatic speech recognition. They inherently incorporate the sequential character of the speech signal and are statistically trained. ...
In this paper, a discriminant hidden Markov model is defined and it is shown how a particular multilayer perceptron with contextual and extra feedback input units can be considered as a general form of ...
INTRODUCTION Hidden Markov models (HMM) [Jelinek, 1976; Bourlard et al., 1985] are widely used for automatic isolated and connected speech recognition. ...
doi:10.1109/34.62605
fatcat:rwio22plvjbd5e22l22zjrcjim
Page 1847 of Linguistics and Language Behavior Abstracts: LLBA Vol. 28, Issue 4
[page]
1994
Linguistics and Language Behavior Abstracts: LLBA
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(Dept Electronica y Tecnologia Computadores U Granada, E-18071 Spain (Tel: 34-58-243283; Fax: 34-58-243230; e-mail: segura @hal.ugr.es}), Multiple VQ Hidden Markov Modelling for Speech Recognition, Speech ...
Communication, 1994, 14, 2, Apr, 119-130. 1 A new way to model context dependencies in speech is presented within the framework of phonemic speech recognition using hidden Markov models (HMMs) together ...
A Review on Speech Recognition Technique
2010
International Journal of Computer Applications
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Speech has potential of being important mode of interaction with computer .This paper gives an overview of major technological perspective and appreciation of the fundamental progress of speech recognition ...
and also gives overview technique developed in each stage of speech recognition. ...
The authors would like to thank the university authorities for Providing infrastructure to carry out the experiments. This work is supported by DST. ...
doi:10.5120/1462-1976
fatcat:zx4z3uczqjh2hhnrtc46b3rycu
Hidden Neural Network for Complex Pattern Recognition: A Comparison Study with Multi- Neural Network Based Approach
2013
International Journal of Life Science and Medical Research
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Each category is modeled by a discrete HMM with KM clustering. For the phase of generalization, the authors used a global DB. ...
RBF) as a classifier. (2) Hybrid HMM/MLP model using a Multi Layer-Perceptron (MLP) to estimate the Hidden Markov Models (HMM) emission probabilities. ...
Second, we present the Hidden Markov Models (HMM) and apply them to complex pattern recognition problem. ...
doi:10.5963/lsmr0306003
fatcat:e63nnycsxvf6zawzqaqgselr7i
Discrete-Mixture HMMs-based Approach for Noisy Speech Recognition
[chapter]
2007
Robust Speech Recognition and Understanding
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Meanwhile, the discrete Hidden Markov Models (DHMMs) based on vector quantization (VQ) have a problem that they are effected by quantization distortion. ...
Introduction It is well known that the application of hidden Markov models (HMMs) has led to a dramatic increase of the performance of automatic speech recognition in the 1980s and from that time onwards ...
The first four chapters address the task of voice activity detection which is considered an important issue for all speech recognition systems. ...
doi:10.5772/4749
fatcat:6inybks2mjhp3gc7tjivnh4lya
Recognizing articulatory gestures from speech for robust speech recognition
2012
Journal of the Acoustical Society of America
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In the second stage, gesture-recognition models were applied to natural speech waveforms and word recognition experiments revealed that the recognized gestures can improve the noise-robustness of a word ...
This paper proposes a neural network architecture for recognizing articulatory gestures from speech and presents ways to incorporate articulatory gestures for a digit recognition task. ...
=2; t þ d=2 ½ ms is stacked with the feature vector at t ms to form a contextualized super-vector. ...
doi:10.1121/1.3682038
pmid:22423722
fatcat:ahfiboazp5hldccsm2fqurm4iy
A Continuous Speech Recognition System Embedding MLP into HMM
1989
Neural Information Processing Systems
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We are developing a phoneme based. speaker-dependent continuous speech recognition system embedding a Multilayer Perceptron (MLP) (Le .• a feedforward Artificial Neural Network). into a Hidden Markov Model ...
It is shown here that word recognition performance for a simple discrete density HMM system appears to be somewhat better when MLP methods are used to estimate the emission probabilities. ...
Acknowledgments Support from the International Computer Science Institute (ICSI) and Philips Research for this work is gratefully acknowledged. ...
dblp:conf/nips/BourlardM89
fatcat:ggvjqqxxcrai5lufnrse5ahcru
Continuous speech recognition using hidden Markov models
1990
IEEE ASSP Magazine
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Since t h e i n t r o d u c t i o n of Markov models t o speech processing in t h e middle 1970s. continuous speech recognition technology has come of age. ...
In this paper, w e review the use of Markov models in continuous speech recognition. Markov models are presented as a generalization of i t s predecessor technology, Dynamic Programming. ...
Fig 2 A bottom-up DP speech recognition system that providl
AFig. 4 . 4 Dynamic An illustration of the similarity between Dynamic Programming and Hidden Markov model based time normalization. ...
doi:10.1109/53.54527
fatcat:4qr2kggggvgflaq3ti3kxbbq3y
State of the art in continuous speech recognition
1995
Proceedings of the National Academy of Sciences of the United States of America
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This paper focuses on the speech recognition advances made through better speech modeling techniques, representing resonances of the vocal tract. ...
and more powerful computers), have combined to make high-accuracy, speakerindependent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without ...
Each of these models is a hidden Markov model, which is discussed below.
HIDDEN MARKOV MODELS Markov Chains. ...
doi:10.1073/pnas.92.22.9956
pmid:7479809
pmcid:PMC40718
fatcat:jkialvbrdrdd5ktr76p52mp5kq
A method for noise-robust context-aware pattern discovery and recognition from categorical sequences
2012
Pattern Recognition
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An efficient method for weakly supervised pattern discovery and recognition from discrete categorical sequences is introduced. ...
The capabilities of the algorithm are demonstrated in a keyword learning task from continuous infantdirected speech and a continuous speech recognition task operating at varying noise levels. ...
The authors would also like to thank the two anonymous reviewers for their useful comments on the manuscript. ...
doi:10.1016/j.patcog.2011.05.005
fatcat:34qc32gmongmrdzlsw2fwvsoje
Exploiting contextual information for improved phoneme recognition
2008
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
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In this paper, we investigate the significance of contextual information in a phoneme recognition system using the hidden Markov model -artificial neural network paradigm. ...
The best phoneme (excluding silence) recognition accuracy of 73.4% on the TIMIT database is comparable to that of the state-ofthe-art systems, but more emphasis is on analysis of the contextual information ...
SUMMARY AND CONCLUSIONS In this paper, we further investigate the hidden Markov model -artificial neural network paradigm for phoneme recognition and analyse the contextual information at the features ...
doi:10.1109/icassp.2008.4518643
dblp:conf/icassp/PintoYHM08
fatcat:4ycw5vodbrhv5fn3j3px3rsde4
Dynamic Speech Models: Theory, Algorithms, and Applications
2006
Synthesis Lectures on Speech and Audio Processing
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The answers to all these questions require building and applying computational models for the dynamic speech process. What are the compelling reasons for carrying out dynamic speech modeling? ...
This speech "chain" starts with the formation of a linguistic message in a speaker's brain and ends with the arrival of the message in a listener's brain. ...
Quantization Scheme for the Hidden Dynamic Vector In the discretized hidden dynamic model, which is the theme of this chapter, the discretization scheme is a central issue. ...
doi:10.2200/s00028ed1v01y200605sap002
fatcat:yi2vjajqvrbl3cpme46o6ivkcq
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