Comparison Between Features Extraction Techniques for Impairments Arabic Speech

Shareef, Sura Ramzi; Al-Irhayim, Yousra Faisal Muhammad

doi:10.33899/rengj.2022.132977.1160

	Comparison Between Features Extraction Techniques for Impairments Arabic Speech
Al-Rafidain Engineering Journal (AREJ)
Article 18, Volume 27, Issue 2, 2022, Pages 190-197 PDF (1.31 M)
Document Type: Research Paper
DOI: 10.33899/rengj.2022.132977.1160
Authors
Sura Ramzi Shareef^* ¹; Yousra Faisal Muhammad Al-Irhayim²
¹Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq
²Computer Science Departement, Collage of Computer Science and Mathematics, University of Mosul, Mosul, Iraq
Abstract
Automatic Speech Recognition (ASR) is a tough task, with the existence of related noise and high unpredictability in a speech presenting the most severe problems. Especially with regard to the noise of speech impairments, whether due to disability or mispronunciation in children. Extraction of noise-resistant features to compensate for speech degradation due to noise impact has remained a difficult challenge in the last few years. This research investigated the impact of different wavelet generations for extracting speech features, then test the produced dataset from each technique with two types of deep learning techniques: deep long short-term memory (LSTM) and hyper deep learning model convolutional neural network with long short-term memory (CNN-LSTM). The result shows that the deep long short-term memory of MFCC has reached 93% as an accuracy while in the hyper deep learning model of CNN-LSTM the accuracy of MFCC was 91%, as the highest recorded accuracy which proves that MFCC would be the best feature extraction technique for our developed dataset.
Keywords
features; Impairments; Arabic; Speech; extraction

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