Improved hidden Markov model for speech recognition and POS tagging

Yuan, Lichi

doi:10.1007/s11771-012-1033-2

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…Analysing the base Viterbi algorithm, we have found that, one step t (t = 2,...,T), Viterbi algorithm have to calculate a probability of a word to take out Max a formula (3).…”

Section: Algorithm Analysismentioning

confidence: 99%

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A Formula to Calculate Pruning Threshold for the Part-of-Speech Tagging Problem

Hieu¹

2018

JST

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The exact tagging of the words in the texts is a very important task in the natural language processing. It can support parsing the text, contribute to the solution of the polysemous word, and help to access a semantic information, etc. One of crucial factors in the POS (Part-ofSpeech) tagging approaches based on the statistical method is the processing time. In this paper, we propose an approach to calculate the pruning threshold, which can apply into the Viterbi algorithm of Hidden Markov model for tagging the texts in the natural language processing. Experiment on the 1.000.000 words on the tag of the Wall Street Journal corpus showed that our proposed solution is satisfactory.

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“…Analysing the base Viterbi algorithm, we have found that, one step t (t = 2,...,T), Viterbi algorithm have to calculate a probability of a word to take out Max a formula (3).…”

Section: Algorithm Analysismentioning

confidence: 99%

“…Among the above approaches, one based on the Hidden Markov model (HMM) can offer prominent results [3]. Especially, when using the Viterbi algorithm, it can achieve an accuracy rate of over 95 percent [4].…”

Section: Introductionmentioning

confidence: 99%

A Formula to Calculate Pruning Threshold for the Part-of-Speech Tagging Problem

Hieu¹

2018

JST

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“…Speech signal is regarded to be short-time stationary [2][3][4][5][6] . Thus, we can use the modern signal process technology to deal with speech signal.…”

Section: Framing and Windowing Of Speech Signalmentioning

confidence: 99%

“…HMM algorithm used in speech recognition mainly started 30 years ago. There have been many research fruits on its application to speech recognition field since 1980 [3,4] . HMM algorithm is a probability model used to describe statistical properties of the stochastic processes, which is generally represented by parameters [4,5] .…”

Section: Introductionmentioning

confidence: 99%

“…There have been many research fruits on its application to speech recognition field since 1980 [3,4] . HMM algorithm is a probability model used to describe statistical properties of the stochastic processes, which is generally represented by parameters [4,5] . HMM models express the acoustic model of speech relatively based on the idea of statistics, and it puts the bottom of the acoustic model and the top of the voice model into unified search algorithm for speech recognition [2] .…”

Section: Introductionmentioning

confidence: 99%

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A Study on Speech Recognition for Design Intent of Geometric Modeling Using HMM and its Application

Chen

Wang

Yang

et al. 2013

AMM

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Design automation is always one of research focuses in the field of industry practices. With the development of computer science and technology, speech recognition technology attracts increasing researchers, and it is widely applied to industrial production. Around design automation, we propose a geometry modeling method through design intent using speech recognition, which is an automatic and efficient geometry modeling method. This paper discusses the speech recognition for design intent of geometry modeling using Hidden Markov Model, HMM. In this paper, firstly, the research framework is explained in detail; Secondly, analysis on the related theory and technology about the speech recognition are conducted, including speech signal collection, technology of data processing, technology of training and recognition of model, etc.; Thirdly, some experiments are carried out to validate above discussions; Finally, recognition efficiency comparisons between HMM and DTW are discussed. Experiments show that using HMM for the speech recognition of design intent is feasible and reasonable.

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Comparison of dynamic Bayesian network approaches for online diagnosis of aircraft system

Feng

Liu

2016

J. Cent. South Univ.

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Improved hidden Markov model for speech recognition and POS tagging

Cited by 7 publications

References 13 publications

A Formula to Calculate Pruning Threshold for the Part-of-Speech Tagging Problem

A Formula to Calculate Pruning Threshold for the Part-of-Speech Tagging Problem

A Study on Speech Recognition for Design Intent of Geometric Modeling Using HMM and its Application

Comparison of dynamic Bayesian network approaches for online diagnosis of aircraft system

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