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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| Format: | Final Year Project / Dissertation / Thesis |
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2022
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| Online Access: | http://eprints.utar.edu.my/4657/ http://eprints.utar.edu.my/4657/1/fyp_CS_2022_LSY.pdf |
| _version_ | 1848886208307920896 |
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| author | Len, Shu Yuan |
| author_facet | Len, Shu Yuan |
| author_sort | Len, Shu Yuan |
| building | UTAR Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:34:50Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-4657 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:34:50Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-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/ |
| spellingShingle | Q Science (General) T Technology (General) Len, Shu Yuan Improving Speech-to-Text recognition for Malaysian english accents using accent identification |
| title | Improving Speech-to-Text recognition for Malaysian english accents using accent identification
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| 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_short | 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 |
| topic | Q Science (General) T Technology (General) |
| url | http://eprints.utar.edu.my/4657/ http://eprints.utar.edu.my/4657/1/fyp_CS_2022_LSY.pdf |