research-article

Spoken language identification based on optimised genetic algorithm–extreme learning machine approach

Authors:

Musatafa Abbas Abbood Albadr,

Fahad Taha AL-DhiefAuthors Info & Claims

Volume 22, Issue 3

Pages 711 - 727

https://doi.org/10.1007/s10772-019-09621-w

Published: 01 September 2019 Publication History

Abstract

The determination and classification of a recognized spoken language based on certain contents and datasets is known as the process of language identification (LID). The common process in carrying out LID entails the mandatory processing of data which enables the extraction of the necessary features for the process. The extraction involves a mature process whereby the development of the standard LID features have been conducted much earlier by means of a mel-frequency cepstral coefficients, shifted delta cepstral, Gaussian mixture model and i-vector-based framework. Despite that, improvement or rather optimisation still needs to be done on the learning process based on the extracted features so as to obtain all the knowledge embedded within them. The classification and regression analysis can benefit tremendously from the use of the extreme learning machine (ELM) which is a particularly effective and useful learning model for training a single-hidden layer neural network. However, owing to the randomly selected weights embedded in the input’s hidden layers, the model’s learning process is rendered to be ineffective or not optimised in its entirety. In this study, the ELM is employed as the learning model for LID due to the standard feature extraction. In addition, this study proposes a new optimised genetic algorithm (OGA) with three different selection criteria (i.e., roulette wheel, K-tournament and random) to select the appropriate initial weights and biases of the input hidden layer of the ELM, thereby minimising the classification error and improving the general performance of the ELM for LID. Results show the excellent performance of the proposed OGA–ELM with three different selection criteria, namely, roulette wheel, K-tournament and random, with the highest accuracies of 99.50%, 100% and 99.38%, respectively.

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Cited By

Zakariah MAl-Razgan MAlfakih T(2024)Pathological voice classification using MEEL features and SVM-TabNet modelSpeech Communication10.1016/j.specom.2024.103100162:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.specom.2024.103100
Godslove JNayak A(2024)Trilingual conversational intent decoding for response retrievalKnowledge and Information Systems10.1007/s10115-023-01972-w66:1(535-556)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10115-023-01972-w
Albadr MAL-Dhief FMan LArram AAbbas AHomod R(2024)Online sequential extreme learning machine approach for breast cancer diagnosisNeural Computing and Applications10.1007/s00521-024-09617-x36:18(10413-10429)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09617-x
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Information & Contributors

Information

Published In

cover image International Journal of Speech Technology

International Journal of Speech Technology Volume 22, Issue 3

Sep 2019

364 pages

ISSN:1381-2416

Issue’s Table of Contents

Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2019

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Cited By

Zakariah MAl-Razgan MAlfakih T(2024)Pathological voice classification using MEEL features and SVM-TabNet modelSpeech Communication10.1016/j.specom.2024.103100162:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.specom.2024.103100
Godslove JNayak A(2024)Trilingual conversational intent decoding for response retrievalKnowledge and Information Systems10.1007/s10115-023-01972-w66:1(535-556)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10115-023-01972-w
Albadr MAL-Dhief FMan LArram AAbbas AHomod R(2024)Online sequential extreme learning machine approach for breast cancer diagnosisNeural Computing and Applications10.1007/s00521-024-09617-x36:18(10413-10429)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09617-x
Moradi AShekofteh Y(2023)Spoken language identification using a genetic-based fusion approach to combine acoustic and universal phonetic resultsComputers and Electrical Engineering10.1016/j.compeleceng.2022.108549105:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.compeleceng.2022.108549
Albadr MTiun SAyob MNazri MAL-Dhief F(2023)Grey wolf optimization-extreme learning machine for automatic spoken language identificationMultimedia Tools and Applications10.1007/s11042-023-14473-382:18(27165-27191)Online publication date: 8-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14473-3
Song Q(2022)Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set NetworkComputational Intelligence and Neuroscience10.1155/2022/72818922022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7281892
Thukroo IBashir RGiri K(2022)A review into deep learning techniques for spoken language identificationMultimedia Tools and Applications10.1007/s11042-022-13054-081:22(32593-32624)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-13054-0
Albadr MTiun SAyob MAL-Dhief FOmar KMaen M(2022)Speech emotion recognition using optimized genetic algorithm-extreme learning machineMultimedia Tools and Applications10.1007/s11042-022-12747-w81:17(23963-23989)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-022-12747-w
Bai LLi HGao WXie J(2022)A cooperative genetic algorithm based on extreme learning machine for data classificationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07202-926:17(8585-8601)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s00500-022-07202-9
Albadr MTiun S(2020)Spoken Language Identification Based on Particle Swarm Optimisation–Extreme Learning Machine ApproachCircuits, Systems, and Signal Processing10.1007/s00034-020-01388-939:9(4596-4622)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s00034-020-01388-9

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