Some improvements in speech recognition algorithms based on HMM

A recognizer for isolated words based on second order hidden Markov models (HMMs) is presented. This system is compared with the usual first order one. To evaluate the performances of both systems, a database of French digits is used. Results are more accurate with the recognizer using second-order hidden Markov process. A version of the Viterbi algorithm which is suited to continuous-speech recognition is described. This version, called Viterbi-block allows segmentation and labeling of an unknown utterance during its processing, whereas the classical Viterbi algorithm waits until the end of the sentence before backtracking the best path. This algorithm is based on a local optimum comparison of the different probabilities computed by the Viterbi algorithm during the time warping of a shift window of fixed length in the signal and the different HMMs. Different criteria of comparison between the probabilities of different paths are tried and assessed