Hardware Implementation of Probabilistic State Machine for Word Recognition
نویسنده
چکیده
Probabilistic Finite State Machines (PFSM) are used in feature Extraction, training and testing which are the most important steps in any speech recognition system. An important PFSM is the Hidden Markov Model which is dealt in this paper. This paper proposes a hardware architecture for the forward-backward algorithm as well as the Viterbi Algorithm used in speech recognition based on Hidden Markov models. The feature extraction and the training process is done using Hidden Markov Model (HTK) tool kit. The testing of the utterance of a new word is done using the hardware proposed in the paper. Finally the results obtained from the HTK tool kit and the hardware is compared.
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