Patent ID: 7454341

Claim:
A computer implemented method comprising: dividing a mean vector set having a plurality of dimensions into multiple mean sub-vector sets, the mean vector set including a mean vector of one of a set of N Gaussians, wherein the mean vector contributes only a sub-vector of the mean vector to one of the mean sub-vector sets, and wherein a set of all dimensions of the one of the mean sub-vector sets includes only a subset of the plurality of dimensions of the mean vector set; dividing a variance vector set having a plurality of dimensions into multiple variance sub-vector sets, the variance vector set including a variance vector of one of the set of N Gaussians, wherein the variance vector contributes only a sub-vector of the variance vector to one of the variance sub-vector sets, and wherein a set of all dimensions of the one of the variance sub-vector sets includes only a subset of the plurality of dimensions of the variance vector set; clustering each resultant sub-vector set to build a codebook for the respective sub-vector set according to a modified K-means clustering process which, during an iteration of the modified K-means clustering process, dynamically assigns each sub-vector in the respective sub-vector set to a respective cluster in a current cluster set, based upon a size of a particular cluster in the current cluster set, reassigns each sub-vector assigned to the particular cluster to another cluster in the current cluster set, and removes the particular cluster from the current set of clusters to create a new cluster set, and splits a cluster in the new cluster set based upon an average distortion of the cluster in the new cluster set; decoding information related to a speech signal using said clustered sub-vector sets; and providing a set of one or more words corresponding to the speech signal based on the decoded information.