CLUSTERING ACOUSTIC PROTOTYPES WITH SELF ORGANIZING DISTORTION MEASURES.

Abstract

A new algorithm for obtaining a set of acoustic prototypes is described. Associated with each prototype is a distortion measure whose full quadratic distance form is optimized to achieve a local minimum for the average distortion. Using a K-means clustering strategy, it is shown that in each iteration the minimum is achieved when the eigenvectors of the weighting matrices of the quadratic distances are identical to those of the sample covariance matrices of their corresponding clusters. Under certain eigenvalue constraints, closed-form solutions for finding the eigenvalues are provided. It is shown that clustering schemes that assume a multivariate Gaussian mixture density for the data can be solved using the new technique. From this fact a new derivation for the maximum-likelihood estimate of their associated covariance matrices is presented. FInally, recognition results obtained by incorporating the new algorithm in the IBM speech recognition system are presented.