Binary phoneme classification using fixed and adaptive segment- based neural network approach
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 8, Issue 1 (31 Jan. 2011), pp.48-51, 4 p.
Publisher
Publication Date
2011-01-31
Country of Publication
Jordan
No. of Pages
4
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper addresses the problem of binary phoneme classification via a neural net segment-based approach.
Phoneme groups are categorized based on articulatory information.
For an efficient segmental acoustic properties capture, the phoneme associated with a speech segment is represented using MFCC’s features extracted from different portions of that segment as well as its duration.
These portions are obtained with fixed or variable size analysis.
The classification is done with a Multi-Layer Perceptron trained using the Mackay’s Bayesian approach.
Experimental results obtained from the Otago speech corpus favourites the use of fixed segmentation strategies over adaptive ones for resolving consonants / vowels, Fricatives / non fricatives, nasals/non nasals and stops/non-stops binary classification problems.
American Psychological Association (APA)
Messikh, Lutfi& Bedda, Mouldi. 2011. Binary phoneme classification using fixed and adaptive segment- based neural network approach. The International Arab Journal of Information Technology،Vol. 8, no. 1, pp.48-51.
https://search.emarefa.net/detail/BIM-244504
Modern Language Association (MLA)
Messikh, Lutfi& Bedda, Mouldi. Binary phoneme classification using fixed and adaptive segment- based neural network approach. The International Arab Journal of Information Technology Vol. 8, no. 1 (Jan. 2011), pp.48-51.
https://search.emarefa.net/detail/BIM-244504
American Medical Association (AMA)
Messikh, Lutfi& Bedda, Mouldi. Binary phoneme classification using fixed and adaptive segment- based neural network approach. The International Arab Journal of Information Technology. 2011. Vol. 8, no. 1, pp.48-51.
https://search.emarefa.net/detail/BIM-244504
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 50-51
Record ID
BIM-244504