NDLI: A partitioned neural network approach for vowel classification using smoothed time/frequency features

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A partitioned neural network approach for vowel classification using smoothed time/frequency features

Content Provider	IEEE Xplore Digital Library
Author	Zahorian, S.A. Nossair, Z.B.
Copyright Year	1993
Abstract	A novel pattern classification technique and a new feature extraction method are described and tested for vowel classification. The pattern classification technique partitions an N-way classification task into N*(N-1)/2 two-way classification tasks. Each two-way classification task is performed using a neural network classifier that is trained to discriminate the two members of one pair of categories. Multiple two way classification decisions are then combined to form an N-way decision. Some of the advantages of the new classification approach include the partitioning of the task allowing independent feature and classifier optimization for each pair of categories, lowered sensitivity of classification performance on network parameters, a reduction in the amount of training data required, and potential for superior performance relative to a single large network. The features described in this paper, closely related to the cepstral coefficients and delta cepstra commonly used in speech analysis, are developed using a unified mathematical framework which allows arbitrary nonlinear frequency, amplitude, and time scales to compactly represent the spectral/temporal characteristics of speech. This classification approach, combined with a feature ranking algorithm which selected the 35 most discriminative spectral/temporal features for each vowel pair, resulted in 71.5% accuracy for classification of 16 vowels extracted from the TIMIT database. These results, significantly higher than other published results for the same task, illustrate the potential for the methods presented in this paper.
Starting Page	414
Ending Page	425
Page Count	12
File Size	179520
File Format	PDF
ISSN	10636676
Volume Number	7
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Frequency Training data Speech analysis Automatic speech recognition Pattern classification Feature extraction Testing Cepstral analysis Spatial databases
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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