Patent ID: 7590537
Filing Date: 2009-09-15
Classification: G10L

Abstract:
1. 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: 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; 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.