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Journal Article A Deconvolutive Neural Network for Speech Classification With Applications to Home Service Robot
Cited 21 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Donglin Wang, Henry Leung, Ajeesh P. Kurian, Hye-Jin Kim, Hosub Yoon
Issue Date
2010-12
Citation
IEEE Transactions on Instrumentation and Measurement, v.59, no.12, pp.3237-3243
ISSN
0018-9456
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/TIM.2010.2047551
Abstract
Reverberation deteriorates the quality and intelligibility of speech, leading to the poor performance of classification systems. Room reverberation parameters depend on the location of the speaker and the microphone and the room geometry. For mobile robots, the reverberation is constantly changing due to the relative movement of the speaker and the robot. This can affect the spectral properties of the signal and therefore, the classification accuracy. The contribution of this paper is a new network architecture, which uses neural network constant modulus algorithm (NNCMA) based equalizer followed by a multi-layer preceptron (MLP) classifier. NNCMA is an MLP which is trained with a cost function similar to constant modulus algorithm (CMA). With this two-stage structure, the classifier does not have to consider the time-varying nature of the reverberation. The proposed algorithm is applied to speech samples collected by the home service robot WEVER-R2 for speaker classification in a typical home or office environment. We use them for gender classification application. The proposed neural network was found to have 83.73% of classification accuracy for age classification and 88.91% of classification accuracy for gender classification, while the standard MLP had a classification accuracy of 71.43% and 72.29%, respectively. © 2006 IEEE.
KSP Keywords
Age Classification, Classification system, Constant Modulus Algorithm(CMA), Cost Function, Gender Classification, Mobile robots, Multi-layer Preceptron(MLP), Network Architecture, Relative movement, Room Reverberation, Room geometry