Evaluating the effect of voice activity detection in isolated Yoruba word recognition system

This paper discusses and evaluates the effect of voice Activity Detection (VAD) in an isolated Yoruba word recognition system (IYWRS). The word database used in this paper are collected from 22 speakers by repeating the numbers 1 to 9 three times each. A hybrid configuration of Mel-Frequency Cepstra...

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Bibliographic Details
Published in2011 4th International Conference on Mechatronics (ICOM) pp. 1 - 5
Main Authors Aibinu, A. M., Salami, M. J. E., Najeeb, A. R., Azeez, J. F., Rajin, S. M. A. K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2011
Subjects
Accuracy
Artificial neural networks
Feature extraction
Mel frequency cepstral coefficient
Speech
Speech recognition
Training
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Summary:This paper discusses and evaluates the effect of voice Activity Detection (VAD) in an isolated Yoruba word recognition system (IYWRS). The word database used in this paper are collected from 22 speakers by repeating the numbers 1 to 9 three times each. A hybrid configuration of Mel-Frequency Cepstral coefficient (MFCC) and Linear Predictive Coding (LPC) have been used to extract the features of the speech samples. Artificial Neural Network algorithms are then used to classify these features. An overall accuracy of about 60% has been achieved from the two proposed feature extraction methods.
ISBN:1612844359
9781612844350
DOI:10.1109/ICOM.2011.5937134