Samsung Electronics Co., Ltd.

Sangbae Jeong

Icksang Han

Jeongsu Kim

Eugene Jon

A probability density function compensation method used for a continuous hidden Markov model and a speech recognition method and apparatus, the probability density function compensation method including extracting feature vectors from speech signals, and using the extracted feature vectors, training a model having a plurality of probability density functions to increase probabilities of recognizing the speech signals; obtaining a global variance by averaging variances of the plurality of the probability density functions after completing the training; obtaining a compensation factor using the global variance; and applying the global variance to each of the probability density functions and compensating each of the probability density functions for the global variance using the compensation factor.

Probability density function compensation method for hidden markov model and speech recognition method and apparatus using the same

An apparatus, medium, and method recognizing speech. The method may include the calculating of scores indicating the degree of similarity between a characteristic of an input speech and characteristics of speech models based on the degree of similarity between the length of each phoneme included in an input speech and the length of phonemes included in each speech model, and determining a speech model with the highest score among the scores to be the corresponding recognized speech for the input speech. By doing so, the speech recognition rate may be greatly enhanced and when an input speech includes continuous identical phonemes the word error rate (WER) may be greatly reduced.

Growing community of inventors

Suwon-si, South Korea

Sangbae Jeong