Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini

Sarbini, Irdhan (2005) Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini. Degree thesis, Universiti Teknologi MARA.

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Abstract

In this research, a prototype of isolated Malay word speech recognition using recurrent neural network (RNN) is proposed. The research is working on speaker independent, which is combination of male and female respondent. A simple three-layer RNN which is Elman Network is employed to learn the pattern of speech features. Melfrequency Cepstral Coefficient (MFCC) feature is selected and the features are extracted by using Speech Filing System freeware application. Experiments are performed to determine the optimal number of hidden neurons for the architecture of RNN. The total recognition rate is 95 %. This research also reveals that RNN is able to give good performance for speech recognition and for incomplete data.

Item Type: Thesis (Degree)
Creators:
CreatorsEmail
Sarbini, IrdhanUNSPECIFIED
Divisions: Faculty of Information Technology and Quantitative Sciences
Item ID: 1496
Uncontrolled Keywords: Malay words, Speech recognition, Recurrent neural network (RNN), Elman network, Melfrequency Cepstral Coefficient (MFCC), Malaysia
Last Modified: 30 May 2017 07:32
Depositing User: Staf Pendigitalan 1
URI: http://ir.uitm.edu.my/id/eprint/1496

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