Isolated Word Recognition for English Language Using LPC, VQ and HMM

Speech recognition is always looked upon as a fascinating field in human computer interaction. It is one of the fundamental steps towards understanding human cognition and their behaviour.This report explicates the theory and implementation of ASR, which is a speaker-dependent real time isolated word recognizer .The major logic used was to first obtain the feature vectors using LPC which was followed by vector quantization. The quantized vectors were then recognized by a suitable modeling technique namely HMM .In the recognition phase the Baum Welch algorithm was used. However it was soon realized that unless some normalization or scaling was carried out the results were highly inaccurate. The paper proposes certain significant modifications to the already existing scaling algorithms .These modifications were brought about after an extensive research work. The results suggest that optimal scaling computations significantly improve the recognition. The schema proposes this modified algorithm which leads to a new insight in the speech recognition techniques.