RECOGNITION OF SPEECH SIGNALS: AN EXPERIMENTAL COMPARISON OF LINEAR PREDICTIVE CODING AND DISCRETE WAVELET TRANSFORMS

摘要

In this paper, a speech recognition system is developed using two different feature extraction techniques and a comparative study is 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 Discrete Wavelet Transforms (DWT) 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. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But discrete wavelet transforms are found to be more suitable for recognizing speech because of their multi-resolution characteristics and efficient time frequency localizations.