UTAR Institutional Repository

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

Len, Shu Yuan (2022) Improving Speech-to-Text recognition for Malaysian english accents using accent identification. Final Year Project, UTAR.

Download (1828Kb) | Preview


    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.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: Q Science > Q Science (General)
    T Technology > T Technology (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Honours)
    Depositing User: ML Main Library
    Date Deposited: 15 Jan 2023 21:27
    Last Modified: 15 Jan 2023 21:27
    URI: http://eprints.utar.edu.my/id/eprint/4657

    Actions (login required)

    View Item