NDLI: Neural Networks and TEO Features for an Automatic Recognition of Stress in Spontaneous Speech

Content Provider	IEEE Xplore Digital Library
Author	Ling He Lech, M. Maddage, N.C. Allen, N.
Copyright Year	2009
Abstract	This study presents automatic stress recognition methods based on acoustic speech analysis. Novel approaches to feature extraction based on the nonlinear Teager energy operator (TEO) calculated within critical bands, discrete wavelet transform bands, and wavelet packet bands are presented. The classification process was performed using two types of neural networks: the multilayer perceptron neural network (MLPNN) and the probabilistic neural network (PNN). The classification efficiency was tested using the actual stress dataset from the SUSAS database. The speech recordings were made by 15 speakers (8 females and 7 males) reading a list of 35 words under three actual conditions: high stress, low stress, and neutral. The best overall performance was observed for the features extracted using the TEO parameters calculated within perceptual wavelet packet bands (TEO-PWP). Depending on the type of mother wavelet, the correct classification scores for the PWP features ranged from 71.24% to 91.56% (using the MLPNN classifier), and from 86.63% to 93.67% (using the PNN). The PNN classifier outperformed the MLPNN classification method.
Starting Page	227
Ending Page	231
File Size	829444
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech analysis stress and emotion recognition Discrete wavelet transforms TEO features Stress Automatic speech recognition Multi-layer neural network speech classification MLPNN PNN Neural networks Multilayer perceptrons Feature extraction Wavelet packets Testing
Content Type	Text
Resource Type	Article

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