Combination of vector quantization and hidden Markov models for Arabic speech recognition

We present experiments performed to recognize isolated Arabic words. Our recognition system is based on a combination of the vector quantization technique at the acoustic level and Markovian modelling. Hidden Markov models (HMMs) are widely used in a number of practical applications and are especially suitable in speech recognition because of their ability to handle variability of the speech signal. In our system, a word is analysed and represented as a set of acoustic vectors, then transformed into a symbolic sequence using the vector quantizer. This observation sequence is compared to reference Markov models. The word associated with the model obtaining the highest score is declared to be the recognized word