ISOLATED WORD RECOGNITION USING LPC & VECTOR QUANTIZATION

Abstract

Speech recognition is always looked upon as a fascinating field in human computer interaction. It is one of the fundamental steps towards understanding human recognition and their behavior. This paper explicates the theory and implementation of Speech recognition. This 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 measuring the Minimum average distortion. All Speech Recognition systems contain Two Main Phases, namely Training Phase and Testing Phase. In the Training Phase, the Features of the words are extracted and during the recognition phase feature matching Takes place. The feature or the template thus extracted is stored in the data base, during the recognition phase the extracted features are compared with the template in the database. The features of the words are extracted by using LPC analysis. Vector Quantization is used for generating the code books. Finally the recognition decision is made based on the matching score. MATLAB will be used to implement this concept to achieve further understanding.