Deep learning for environmentally robust speech recognition

Alhamada, A. I. and Khalifa, Othman Omran (2020) Deep learning for environmentally robust speech recognition. In: 7th International Conference on Electronic Devices, Systems and Applications (ICEDSA2020), 28th - 29th March 2020, Grand Bluewave Hotel, Shah Alam, Malaysia. (Unpublished)

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Abstract

Deep learning is an emerging technology that is one of the most promising areas of artificial intelligence. Great strides have been made in recent years which resulted in increased efficiency with regards to many applications, including speech. Despite this, an environmentally Robust Speech Recognition system is still far from being achieved. In this article, an investigation of previous work has been conducted. The use of deep learning in speech recognition was analyzed and a proper deep learning architecture was identified. A method using convolutional neural network (CNN) is used with the aim of enhancing the performance of speech recognition systems (SRS). This study found that this CNN-based approach achieves a 94.32% validated accuracy.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	4119/82387
Uncontrolled Keywords:	Deep learning, Robust Speech Recognition, convolutional neural network (CNN), speech recognition systems (SRS)
Subjects:	T Technology > T Technology (General) T Technology > TD Environmental technology. Sanitary engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr Othman O. Khalifa
Date Deposited:	27 Aug 2020 15:07
Last Modified:	30 Dec 2020 15:20
URI:	http://irep.iium.edu.my/id/eprint/82387

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