Improving Speech-to-Text recognition for Malaysian english accents using accent identification

Automatic Speech Recognition (ASR) is the technology that helps user to use their voice as a form of input and it is used in many areas such as mobile devices, embedded systems, and other industrial areas. However, performance and accuracy of the speech recognition system is heavily influenced by th...

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Main Author: Len, Shu Yuan
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Online Access:http://eprints.utar.edu.my/4657/1/fyp_CS_2022_LSY.pdf
http://eprints.utar.edu.my/4657/
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Institution: Universiti Tunku Abdul Rahman
id my-utar-eprints.4657
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spelling my-utar-eprints.46572023-01-15T13:27:08Z Improving Speech-to-Text recognition for Malaysian english accents using accent identification Len, Shu Yuan Q Science (General) T Technology (General) Automatic Speech Recognition (ASR) is the technology that helps user to use their voice as a form of input and it is used in many areas such as mobile devices, embedded systems, and other industrial areas. However, performance and accuracy of the speech recognition system is heavily influenced by the non-native accents, for example, Malaysian English. In this project, the Accent Identification (AID) techniques will be implemented to improve the performance of the ASR systems in recognizing Malaysian English accents. Kaldi toolkits is used in developing proposed ASR models (GMM-HMM and DNN-HMM). CNN based AID is implemented using Python language. The datasets used in this project are from Mini Librispeech, Speech Accent Achieve and other Malaysian English speakers. Then, CNN based AID will be developed and the results is investigated and compared. The Word Error rate is selected as the evaluation metric to compare the recognition performance and accuracy. 2022-04-22 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4657/1/fyp_CS_2022_LSY.pdf Len, Shu Yuan (2022) Improving Speech-to-Text recognition for Malaysian english accents using accent identification. Final Year Project, UTAR. http://eprints.utar.edu.my/4657/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Len, Shu Yuan
Improving Speech-to-Text recognition for Malaysian english accents using accent identification
description Automatic Speech Recognition (ASR) is the technology that helps user to use their voice as a form of input and it is used in many areas such as mobile devices, embedded systems, and other industrial areas. However, performance and accuracy of the speech recognition system is heavily influenced by the non-native accents, for example, Malaysian English. In this project, the Accent Identification (AID) techniques will be implemented to improve the performance of the ASR systems in recognizing Malaysian English accents. Kaldi toolkits is used in developing proposed ASR models (GMM-HMM and DNN-HMM). CNN based AID is implemented using Python language. The datasets used in this project are from Mini Librispeech, Speech Accent Achieve and other Malaysian English speakers. Then, CNN based AID will be developed and the results is investigated and compared. The Word Error rate is selected as the evaluation metric to compare the recognition performance and accuracy.
format Final Year Project / Dissertation / Thesis
author Len, Shu Yuan
author_facet Len, Shu Yuan
author_sort Len, Shu Yuan
title Improving Speech-to-Text recognition for Malaysian english accents using accent identification
title_short Improving Speech-to-Text recognition for Malaysian english accents using accent identification
title_full Improving Speech-to-Text recognition for Malaysian english accents using accent identification
title_fullStr Improving Speech-to-Text recognition for Malaysian english accents using accent identification
title_full_unstemmed Improving Speech-to-Text recognition for Malaysian english accents using accent identification
title_sort improving speech-to-text recognition for malaysian english accents using accent identification
publishDate 2022
url http://eprints.utar.edu.my/4657/1/fyp_CS_2022_LSY.pdf
http://eprints.utar.edu.my/4657/
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