Masanori Omote

Voice recognition device and method using a (GGM) Guaranteed Global minimum Mapping

A voice recognition device according to the present invention including a voice analyzer for acoustically analyzing voice every predetermined frame unit to extract a feature vector X, a converter for subjecting the feature vector X output from the analyzer to a predetermined conversion process, and a voice recognizer for recognizing the voice on the basis of a new feature vector output from the converter, wherein the converter conducts the predetermined conversion processing according to a mapping F from an N-dimensional vector space ΩN to an M-dimensional vector space ΩM, the feature vector X is a vector on the N-dimensional vector space ΩN and the function fm (X) of an m-th component of the mapping F is represented by the following linear summation of the products of functions gm k (X) and coefficients cm k of Lm : ##EQU1## Each function gm k (X) may be set to a monomial.

Stochastic features for noise robust speech recognition

This paper describes a novel technique for noise robust speech recognition, which can incorporate the characteristics of noise distribution directly in features. The feature itself of each analysis frame has a stochastic form, which can represent the probability density function of the estimated speech component in the noisy speech. Using the sequence of the probability density functions of the estimated speech components and hidden Markov modelling of clean speech, the observation probability of the noisy speech is calculated. In the whole process of the technique, the explicit information on the SNR is not used. The technique is evaluated by large vocabulary isolated word recognition under car noise environment, and is found to have clearly outperformed nonlinear spectral subtraction (with between 13% and 44% reduction in recognition errors).