Shift-Tolerant LVQ and Hybrid LVQ-HMM for Phoneme Recognition

Readings in Speech Recognition ◽

10.1016/b978-0-08-051584-7.50041-3 ◽

1990 ◽

pp. 425-438 ◽

Author(s):

Erik McDermott ◽

Hitoshi Iwamida ◽

Shigeru Katagiri ◽

Yoh'ichi Tohkura

Keyword(s):

Phoneme Recognition

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Completely Unsupervised Phoneme Recognition by a Generative Adversarial Network Harmonized with Iteratively Refined Hidden Markov Models

10.21437/interspeech.2019-2068 ◽

2019 ◽

Author(s):

Kuan-Yu Chen ◽

Che-Ping Tsai ◽

Da-Rong Liu ◽

Hung-Yi Lee ◽

Lin-shan Lee

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Phoneme Recognition ◽

Generative Adversarial Network ◽

Adversarial Network

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Hierarchical Neural Network Structures for Phoneme Recognition

10.1007/978-3-642-34425-1 ◽

2013 ◽

Author(s):

Daniel Vasquez ◽

Rainer Gruhn ◽

Wolfgang Minker

Keyword(s):

Neural Network ◽

Network Structures ◽

Phoneme Recognition ◽

Hierarchical Neural Network

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Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

IEEE Access ◽

10.1109/access.2020.3034762 ◽

2020 ◽

Vol 8 ◽

pp. 200395-200411

Author(s):

Ahmed B. Ibrahim ◽

Yasser Mohammad Seddiq ◽

Ali Hamid Meftah ◽

Mansour Alghamdi ◽

Sid-Ahmed Selouani ◽

...

Keyword(s):

Genetic Algorithm ◽

Phoneme Recognition ◽

Phonetic Features

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Effect of aging on speech features and phoneme recognition: a study on Bengali voicing vowels

International Journal of Speech Technology ◽

10.1007/s10772-012-9147-3 ◽

2012 ◽

Vol 16 (1) ◽

pp. 19-31 ◽

Author(s):

Biswajit Das ◽

Sandipan Mandal ◽

Pabitra Mitra ◽

Anupam Basu

Keyword(s):

Phoneme Recognition ◽

Speech Features

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A tutorial on using hidden Markov models for phoneme recognition

Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST '05. ◽

10.1109/ssst.2005.1460896 ◽

2005 ◽

Author(s):

A.C. Veeravalli ◽

W.D. Pan ◽

R. Adhami ◽

P.G. Cox

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Phoneme Recognition

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Contribution from the accuracy of phoneme recognition to the quality of automatic recognition of Russian speech

Moscow University Computational Mathematics and Cybernetics ◽

10.3103/s0278641916020060 ◽

2016 ◽

Vol 40 (2) ◽

pp. 89-95 ◽

Author(s):

I. A. Karpukhin

Keyword(s):

Automatic Recognition ◽

Phoneme Recognition

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Phoneme recognition by cochlear implant users as a function of signal-to-noise ratio and nonlinear amplitude mapping

The Journal of the Acoustical Society of America ◽

10.1121/1.427031 ◽

1999 ◽

Vol 106 (2) ◽

pp. L18-L23 ◽

Author(s):

Qian-Jie Fu ◽

Robert V. Shannon

Keyword(s):

Cochlear Implant ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Phoneme Recognition ◽

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Exploring the Role of the Modulation Spectrum in Phoneme Recognition

Ear and Hearing ◽

10.1097/aud.0b013e31817e73ef ◽

2008 ◽

Vol 29 (5) ◽

pp. 800-813 ◽

Author(s):

Frederick Gallun ◽

Pamela Souza

Keyword(s):

Phoneme Recognition ◽

Modulation Spectrum

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Manifold learning-based phoneme recognition

2009 International Conference on Image Analysis and Signal Processing ◽

10.1109/iasp.2009.5054579 ◽

2009 ◽

Author(s):

JinBiao Chen ◽

Shiqing Zhang

Keyword(s):

Manifold Learning ◽

Phoneme Recognition

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Phoneme recognition using ICA-based feature extraction and transformation

Signal Processing ◽

10.1016/j.sigpro.2004.03.004 ◽

2004 ◽

Vol 84 (6) ◽

pp. 1005-1019 ◽

Author(s):

Oh-Wook Kwon ◽

Te-Won Lee

Keyword(s):

Feature Extraction ◽

Phoneme Recognition

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