NDLI: Phoneme Classification Study for Thai Segment-Based Acoustic Models

Content Provider	IEEE Xplore Digital Library
Author	Ekkachaiworrasin, N. Punyabukkana, P. Suchato, A.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Eng., Chulalongkorn Univ., Bangkok (Ekkachaiworrasin, N.; Punyabukkana, P.; Suchato, A.)
Abstract	This paper presents a study of a phoneme classification experiment in Thai language which is a part of our development of a segment-based speech recognition system. To make feature vectors capture segmental acoustic properties, the phoneme associated with a speech segment is represented using MFCCs extracted from different portions of that segment as well as its duration. The acoustic scoring in our probabilistic framework is composed of finding the probability of a segment belonging to one of a number of phoneme groups and the probability of that segment being a specific phoneme, given the grouping. Phoneme groups are categorized based on manners of articulation and confusions from classification results. Classification using linear discriminant analysis (LDA) with the use of prior probability yields the highest group classification accuracy of 88.5% . When the phoneme group is correctly chosen, the mean probability of a token belonging to that group obtained via LDA is around 0.8. The mean probability is clearly lower when it is incorrectly chosen. This shows that our acoustic scoring of phoneme group has a desirable property
Sponsorship	ECTI, Thailand NECTEC, Thailand King Mongkut's Inst. of Tech., Thailand IEEE Circuits and Syst. Soc
Starting Page	122
Ending Page	127
File Size	7733794
Page Count	6
File Format	PDF
ISBN	0780397401
DOI	10.1109/ISCIT.2006.339900
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-18
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustical engineering Natural languages Hidden Markov models Speech recognition Feature extraction Linear discriminant analysis Electronic mail Data mining Automatic speech recognition
Content Type	Text
Resource Type	Article

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