Automatic English Stop Consonants Classification using Wavelet Analysis and Hidden Markov Models

被引：0

作者：

Kuehne, Marco ^{[1
]}

Togneri, Roberto ^{[1
]}

机构：

[1] Univ Western Australia, Sch Elect Elect & Comp Engn, CIIPS, Nedlands, WA 6009, Australia

来源：

INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5 | 2006年

关键词：

speech recognition; wavelet analysis; Hidden Markov Models;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper compares wavelet and STFT analysis for a speaker-independent stop classification task using the TIMIT database. In the designed experiment the HMM classifier had to assign each test token to one of the following stop classes [d,g,b,t,k,p,dx]. On 6332 stops the wavelet features obtained an overall accuracy of 86 % which corresponds to a 14 % relative error reduction compared to the STFT baseline system. Furthermore an analysis of the HMM misclassifications revealed that voiced stops were highly confused with their voiceless unaspirated counterparts.

引用

页码：637 / 640

页数：4

共 50 条

[1] Maximum likelihood texture analysis and classification using wavelet-domain hidden Markov models
Fan, GL
Xia, XG
[J]. CONFERENCE RECORD OF THE THIRTY-FOURTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, 2000, : 921 - 925
[2] Automatic Classification of Disordered Voices with Hidden Markov Models
Benhammoud, Redouane
Kacha, Abdellah
[J]. 2018 INTERNATIONAL CONFERENCE ON SIGNAL, IMAGE, VISION AND THEIR APPLICATIONS (SIVA), 2018,
[3] Sensorimotor Cortex EEG signal classification using Hidden Markov Models and Wavelet Decomposition
Mumtaz, Maheen
Afzal, Mubashira
Mushtaq, Aleem
[J]. 2018 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), 2018, : 429 - 434
[4] Discrete Wavelet based Features for PCG Signal Classification using Hidden Markov Models
Touahria, Rima
Hacine-Gharbi, Abdenour
Ravier, Philippe
[J]. PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS (ICPRAM), 2021, : 334 - 340
[5] Sensorimotor Cortex EEG signal classification using Hidden Markov Models and Wavelet Decomposition
Mumtaz, Maheen
Afzal, Mubashira
Mushtaq, Aleem
[J]. 2018 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), 2018, : 375 - 381
[6] Automatic fingerprint classification, based on embedded hidden Markov models
Guo, H
Ou, ZY
He, Y
[J]. 2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 3033 - 3038
[7] Automatic classification of environmental noise events by hidden Markov models
Couvreur, C
Fontaine, V
Gaunard, P
Mubikangiey, CG
[J]. APPLIED ACOUSTICS, 1998, 54 (03) : 187 - 206
[8] Hidden Markov and Gaussian mixture models for automatic call classification
Brown, Judith C.
Smaragdis, Paris
[J]. JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2009, 125 (06): : EL221 - EL224
[9] Automatic classification of environmental noise events by hidden Markov models
Gaunard, P
Mubikangiey, CG
Couvreur, C
Fontaine, V
[J]. PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6, 1998, : 3609 - 3612
[10] Classification of chirps using Hidden Markov Models
Balachandran, Nikhil
Creusere, Charles
[J]. 2006 FORTIETH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS, VOLS 1-5, 2006, : 545 - +

← 1 2 3 4 5 →