APPARATUS AND METHODS FOR SPEECH RECOGNITION

摘要

Speech recognisers which employ hidden Markov models using continuous probability functions require a large number of calculations to be carried out in a short time in order to give real time recognition. In addition a large amount of electronic storage is also required. The present invention reduces these problems by reducing the number of probability functions determined and stored by assigning each such function to a reduced number of different states used in the Markov models. In recognising words a minimum distance is computed for each model according to the Viterbi algorithm (operations 45 to 47) but since only a relatively small number of probability functions and states are stored the number of calculations required per unit time is also reduced. Fewer probability functions are stored and thus the amount of storage required is not as great as would otherwise be required. Further in order to reduce costs and increase the speed of recognition a specially constructed Viterbi engine is used in determining the probabilities that sounds observed represent various states of the models. Methods of deriving the required number of probability functions and states are also described.