Patent ID: 7590537

Claim:
A speaker clustering method comprising: extracting a feature vector from speech data of input speech signals of a plurality of training speakers; generating an ML (maximum likelihood) model of the feature vector for the plurality of training speakers; generating model variations of the plurality of training speakers while analyzing a quantity variation amount and/or directional variation amount in an acoustic space of the ML model with respect to a speaker-independent model; generating a plurality of speaker group model variations by applying a predetermined clustering algorithm to the plurality of model variations on the basis of model variations; and generating a variation parameter that is used to generate, in a speech recognition apparatus, a speaker adaptation model with respect to the speaker-independent model, for the plurality of speaker group model variations, wherein the speech recognition apparatus utilizes the speaker adaptation model to output a sentence, and wherein the model variation is represented as follows: D ( x, y )= D Euclidian ( x, y ) α (1−cos θ) where x is a vector of an ML model of a training speaker; y is a vector of a speaker-independent model of a training speaker; D Eucledian ⁡ ( x , y ) =  x - y  2 ; cos ⁢ ⁢ θ = x · y  x  ⁢  y  ; x = [ x 1 , x 2 , … ⁢ , x N ] ; y = [ y 1 , y 2 , … ⁢ , y N ] ; α is a preselected weight; and θ is an angle between the vectors x and y, wherein the generating the variation parameter includes configuring a priori-probability in the case of a maximum a posteriori and a class tree in a case of maximum likelihood linear regression in accordance with the speaker adaptation algorithm.