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162611

Feature Selection for Speaker Identification and Arabic Digits Recognition.

Article

Last updated: 04 Jan 2025

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Tags

Electrical Engineering

Abstract

This article introduces a comparison between three different processing techniques for the selection of speech features.
These features can be used for speaker recognition or speech recognition. A comparison between the performance of a system based on the linear prediction code, a system based on the cepstrum and a system based on the short time energy is introduced feature selection is very effective for recognition accuracy. This work illustrates where each of these features are more efficient for speaker recognition or for speech recognition.
The results show that the short time  energy in time domain is very effective for epeech recognition where its accuracy is found to be 92%. In speaker identification, the accuracy of identification for the features depending on energy in each frame is found to be 60%. It may be recommended that the features based on the capstrum given accuracy of 94% and 96% for speech recognition and speaker identification respectively. The accuracy of linear prediction code features depend on cepstrum may be recommended for speaker identifier or speech recognition.
A recognition system for spoken digits are given using the above features with neural networks. The neural network has been used as a tool in this comparison.

DOI

10.21608/bfemu.2021.162611

Authors

First Name

Mahmoud

Last Name

Abdallah

MiddleName

Ibraheem

Affiliation

Faculty of Engineering., Zagazig University., Zagazig., Egypt.

Email

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City

Zagazig

Orcid

-

First Name

Adel

Last Name

El-Mallawany

MiddleName

-

Affiliation

Acoustic Engineering.,Building Research Center.

Email

-

City

-

Orcid

-

Volume

20

Article Issue

4

Related Issue

23694

Issue Date

1995-12-01

Receive Date

1995-10-11

Publish Date

2021-12-01

Page Start

61

Page End

71

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 20_Issue 4_Pages 61-71.pdf

PDF . 928.7kB

Link

https://bfemu.journals.ekb.eg/article_162611.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=162611

Order

6

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

Feature Selection for Speaker Identification and Arabic Digits Recognition.

Details

Type

Article

Created At

22 Jan 2023