Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman

As Malaysia moves towards to the Industrial Revolution (IR 4.0), and as machines become more intelligent and autonomous, man and machine interaction are becoming inevitable. In general, the machine robustness towards dialect identification will be the main one of the many practical methods for inter...

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Main Author:	Sulaiman, Mohd Azman Hanif
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Malay language General works History Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/75712/1/75712.pdf
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id	my-uitm-ir.75712
record_format	uketd_dc
spelling	my-uitm-ir.757122023-03-30T13:13:57Z Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman 2022 Sulaiman, Mohd Azman Hanif Malay language. General works. History Neural networks (Computer science) As Malaysia moves towards to the Industrial Revolution (IR 4.0), and as machines become more intelligent and autonomous, man and machine interaction are becoming inevitable. In general, the machine robustness towards dialect identification will be the main one of the many practical methods for interaction is using spoken language. However, there are many limitations of this type of interaction, particularly for native speakers other than English among them is dialect criteria for the system development. The complexity of dialects requires a new paradigm/generation of Artificial Intelligence (AI) - based classifiers methods capable of adapting to the linguistic of the language. These proposed techniques are recent and relatively unexplored in the field of dialect identification. This research explores two types of methods for dialect identification, namely Convolution Neural Network (CNN) for Malay dialect identification and MFCC feature extraction technique will be used to extract the features. Next, these features will be transferred to the CNN to be trained. For Long short-term Memory (LSTM), the inputs are fed directly from the recorded dataset for training. This research is to design and implement the CNN and LSTM network for Malay language dialect classification. In support of this, several objectives need to be achieved is to perform features extraction using MFCC on Malaysian Dialect, then to classify the Malaysian Dialect using CNN and LSTM neural networks and compare the performance of CNN and LSTM neural networks on Malay dialect identification. 2022 Thesis https://ir.uitm.edu.my/id/eprint/75712/ https://ir.uitm.edu.my/id/eprint/75712/1/75712.pdf text en public masters Universiti Teknologi MARA (UiTM) College of Engineering Mohd Yassin, Ahmad Ihsan
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohd Yassin, Ahmad Ihsan
topic	Malay language General works History Neural networks (Computer science)
spellingShingle	Malay language General works History Neural networks (Computer science) Sulaiman, Mohd Azman Hanif Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman
description	As Malaysia moves towards to the Industrial Revolution (IR 4.0), and as machines become more intelligent and autonomous, man and machine interaction are becoming inevitable. In general, the machine robustness towards dialect identification will be the main one of the many practical methods for interaction is using spoken language. However, there are many limitations of this type of interaction, particularly for native speakers other than English among them is dialect criteria for the system development. The complexity of dialects requires a new paradigm/generation of Artificial Intelligence (AI) - based classifiers methods capable of adapting to the linguistic of the language. These proposed techniques are recent and relatively unexplored in the field of dialect identification. This research explores two types of methods for dialect identification, namely Convolution Neural Network (CNN) for Malay dialect identification and MFCC feature extraction technique will be used to extract the features. Next, these features will be transferred to the CNN to be trained. For Long short-term Memory (LSTM), the inputs are fed directly from the recorded dataset for training. This research is to design and implement the CNN and LSTM network for Malay language dialect classification. In support of this, several objectives need to be achieved is to perform features extraction using MFCC on Malaysian Dialect, then to classify the Malaysian Dialect using CNN and LSTM neural networks and compare the performance of CNN and LSTM neural networks on Malay dialect identification.
format	Thesis
qualification_level	Master's degree
author	Sulaiman, Mohd Azman Hanif
author_facet	Sulaiman, Mohd Azman Hanif
author_sort	Sulaiman, Mohd Azman Hanif
title	Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman
title_short	Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman
title_full	Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman
title_fullStr	Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman
title_full_unstemmed	Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman
title_sort	malay words and dialect identification using long short-term memory and convolutional neural networks on trained mel frequency cepstral coefficient / mohd azman hanif sulaiman
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Engineering
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/75712/1/75712.pdf
_version_	1783736074259922944

Malay words and dialect identification using long short-term memory and convolutional neural networks on trained Mel frequency cepstral coefficient / Mohd Azman Hanif Sulaiman

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