VOICE RECOGNITION METHOD USING HIDDEN MARKOV MODEL HAVING DISTORTION DENSITY OF OBSERVATION VECTOR

摘要

The present invention relates to speech recognition using distortion of an observation vector, and the distortion probability density during the learning process of a hidden Markov model (HMM) having a distortion probability density of the observation vector. (B) obtaining a state segmentation sequence; calculating an average length mi of each state i with reference to the state segmented learning observation sequence; and a mean vector υ_ij for each normalized time interval j in the state i. Obtaining a distortion eijk for the average vector for each normalization time interval j in each state i for each learning observation sequence k; And obtaining a minimum value ( n_ij ) and a maximum value ( x_ij ) of distortions of the total k learning observation vectors for each normalized time interval j in each state i.;As described above, the present invention provides a better speech recognition rate than the general HMM.