Poulose Jacob,K; Sonia, Sunny; David, Peter S(IEEE, August 9, 2012)
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Abstract:
Speech signals are one of the most important means
of communication among the human beings. In this paper, a
comparative study of two feature extraction techniques are
carried out for recognizing speaker independent spoken
isolated words. First one is a hybrid approach with Linear
Predictive Coding (LPC) and Artificial Neural Networks
(ANN) and the second method uses a combination of Wavelet
Packet Decomposition (WPD) and Artificial Neural Networks.
Voice signals are sampled directly from the microphone and
then they are processed using these two techniques for
extracting the features. Words from Malayalam, one of the
four major Dravidian languages of southern India are chosen
for recognition. Training, testing and pattern recognition are
performed using Artificial Neural Networks. Back propagation
method is used to train the ANN. The proposed method is
implemented for 50 speakers uttering 20 isolated words each.
Both the methods produce good recognition accuracy. But
Wavelet Packet Decomposition is found to be more suitable for
recognizing speech because of its multi-resolution
characteristics and efficient time frequency localizations
Description:
Advances in Computing and Communications (ICACC), 2012 International Conference on