DocumentCode :
1739870
Title :
Endpoint detection of speech signal using neural network
Author :
Hussain, Aini ; Samad, Salina Abdul ; Fah, Liew Ban
Author_Institution :
Fac. of Eng., Kebangsaan Univ., Bangi, Malaysia
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
271
Abstract :
This paper highlights the artificial neural network (ANN) approach to perform the endpoint detection process, which involves the segmentation of speech signals from non-speech signals. Two ANN models have been proposed to perform endpoint detections of isolated digit utterances spoken in the Malay language: multilayer perceptron (MLP) and adaptive linear network (ADALINE). Results obtained from the ANN models are acoustically verified, visually checked and compared to the conventional method of endpoint detection. It was found that the endpoint detection accuracy using the MLP approach is very high and encouraging
Keywords :
feature extraction; multilayer perceptrons; neural nets; speech recognition; ADALINE approach; ANN approach; MLP approach; Malay language; acoustic verification; adaptive linear network; artificial neural network; endpoint detection; isolated digit utterances; multilayer perceptron; nonspeech signals; signal segmentation; speech recognition; speech signal; speech signals; Acoustic signal detection; Adaptive systems; Artificial neural networks; Detectors; Hidden Markov models; Multilayer perceptrons; Neural networks; Signal detection; Signal processing; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON 2000. Proceedings
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6355-8
Type :
conf
DOI :
10.1109/TENCON.2000.893585
Filename :
893585
Link To Document :
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