Endpoint detection of speech signal using neural network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume1
Publication statusPublished - 2000
Event2000 TENCON Proceedings - Kuala Lumpur, Malaysia
Duration: 24 Sep 200027 Sep 2000

Other

Other2000 TENCON Proceedings
CityKuala Lumpur, Malaysia
Period24/9/0027/9/00

Fingerprint

Multilayer neural networks
Neural networks
Linear networks

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Hussain, A., Abdul Samad, S., & Fah, L. B. (2000). Endpoint detection of speech signal using neural network. In IEEE Region 10 Annual International Conference, Proceedings/TENCON (Vol. 1)

Endpoint detection of speech signal using neural network. / Hussain, Aini; Abdul Samad, Salina; Fah, Liew Ban.

IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol. 1 2000.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hussain, A, Abdul Samad, S & Fah, LB 2000, Endpoint detection of speech signal using neural network. in IEEE Region 10 Annual International Conference, Proceedings/TENCON. vol. 1, 2000 TENCON Proceedings, Kuala Lumpur, Malaysia, 24/9/00.
Hussain A, Abdul Samad S, Fah LB. Endpoint detection of speech signal using neural network. In IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol. 1. 2000
Hussain, Aini ; Abdul Samad, Salina ; Fah, Liew Ban. / Endpoint detection of speech signal using neural network. IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol. 1 2000.
@inproceedings{1b2f6f9693b047c08f8543175f48fc6c,
title = "Endpoint detection of speech signal using neural network",
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.",
author = "Aini Hussain and {Abdul Samad}, Salina and Fah, {Liew Ban}",
year = "2000",
language = "English",
volume = "1",
booktitle = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

}

TY - GEN

T1 - Endpoint detection of speech signal using neural network

AU - Hussain, Aini

AU - Abdul Samad, Salina

AU - Fah, Liew Ban

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0034429428&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034429428&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0034429428

VL - 1

BT - IEEE Region 10 Annual International Conference, Proceedings/TENCON

ER -