文章基本信息

标题：Binary Phoneme Classification Using Fixed and Adaptive Segment- Based Neural Network Approach
本地全文：下载
作者：Lotfi Messikh ; Mouldi Bedda
期刊名称：The International Arab Journal of Information Technology
印刷版ISSN：1683-3198
出版年度：2011
卷号：8
期号：1
出版社：Zarqa Private University
摘要：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
关键词：Signal segmentation; binary phoneme classification; segment-based processing; and neural network.