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