A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language

The development of automatic speech recognition (ASR) systems for under-resourced languages poses challenges due to the lack of written resources required to train such systems. Traditionally, researchers have used language models to improve ASR model accuracy, some also resorts to the integration o...

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Main Author:	Steve Olsen, SO, Michael
Format:	Thesis
Language:	English English English
Published:	2024
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/45394/3/DSVA_Steve%20Olsen.pdf http://ir.unimas.my/id/eprint/45394/4/Thesis%20Ms._Steve%20Olsen.ftext.pdf http://ir.unimas.my/id/eprint/45394/5/Thesis%20Ms._Steve%20Olsen%20-%2024%20pages.pdf
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id	my-unimas-ir.45394
record_format	uketd_dc
spelling	my-unimas-ir.453942024-07-26T08:25:54Z A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language 2024-06-30 Steve Olsen, SO, Michael T Technology (General) The development of automatic speech recognition (ASR) systems for under-resourced languages poses challenges due to the lack of written resources required to train such systems. Traditionally, researchers have used language models to improve ASR model accuracy, some also resorts to the integration of pronunciation dictionaries, but these methods require abundance of written resources, which under-resourced languages often lack. The Iban language, spoken by the majority people of Sarawak in Malaysia, is an example of an under-resourced language for which previous attempts at developing an ASR system involved building a pronunciation dictionary and language model, transfer learning, and using DNN-HMM acoustic models. However, these methods proved challenging and costly. In this research, we propose a framework that uses a convolutional neural network (CNN) as an acoustic model to build an end-to-end ASR model for the Iban language. Three techniques are proposed to optimize the model without requiring additional data resources, including hyperparameter optimization, data augmentation and transfer learning. We report a significant reduction in word error rate (WER) in our experiments, demonstrating the effectiveness of our techniques. Overall, the proposed framework offers a promising approach for developing ASR systems for under-resourced languages that lack the necessary written resources for traditional methods. Universiti Malaysia Sarawak 2024-06 Thesis http://ir.unimas.my/id/eprint/45394/ http://ir.unimas.my/id/eprint/45394/3/DSVA_Steve%20Olsen.pdf text en staffonly http://ir.unimas.my/id/eprint/45394/4/Thesis%20Ms._Steve%20Olsen.ftext.pdf text en validuser http://ir.unimas.my/id/eprint/45394/5/Thesis%20Ms._Steve%20Olsen%20-%2024%20pages.pdf text en public masters UNIVERSITI MALAYSIA SARAWAK FACULTY OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (FCSIT)
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English English
topic	T Technology (General)
spellingShingle	T Technology (General) Steve Olsen, SO, Michael A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language
description	The development of automatic speech recognition (ASR) systems for under-resourced languages poses challenges due to the lack of written resources required to train such systems. Traditionally, researchers have used language models to improve ASR model accuracy, some also resorts to the integration of pronunciation dictionaries, but these methods require abundance of written resources, which under-resourced languages often lack. The Iban language, spoken by the majority people of Sarawak in Malaysia, is an example of an under-resourced language for which previous attempts at developing an ASR system involved building a pronunciation dictionary and language model, transfer learning, and using DNN-HMM acoustic models. However, these methods proved challenging and costly. In this research, we propose a framework that uses a convolutional neural network (CNN) as an acoustic model to build an end-to-end ASR model for the Iban language. Three techniques are proposed to optimize the model without requiring additional data resources, including hyperparameter optimization, data augmentation and transfer learning. We report a significant reduction in word error rate (WER) in our experiments, demonstrating the effectiveness of our techniques. Overall, the proposed framework offers a promising approach for developing ASR systems for under-resourced languages that lack the necessary written resources for traditional methods.
format	Thesis
qualification_level	Master's degree
author	Steve Olsen, SO, Michael
author_facet	Steve Olsen, SO, Michael
author_sort	Steve Olsen, SO, Michael
title	A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language
title_short	A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language
title_full	A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language
title_fullStr	A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language
title_full_unstemmed	A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language
title_sort	convolutional neural network (cnn) for automated speed recognition (asr) for low resource language: a case study on iban language
granting_institution	UNIVERSITI MALAYSIA SARAWAK
granting_department	FACULTY OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (FCSIT)
publishDate	2024
url	http://ir.unimas.my/id/eprint/45394/3/DSVA_Steve%20Olsen.pdf http://ir.unimas.my/id/eprint/45394/4/Thesis%20Ms._Steve%20Olsen.ftext.pdf http://ir.unimas.my/id/eprint/45394/5/Thesis%20Ms._Steve%20Olsen%20-%2024%20pages.pdf
_version_	1811771575069835264

A Convolutional Neural Network (CNN) for Automated Speed Recognition (ASR) for Low Resource language: A Case Study on Iban Language

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