Method, apparatus, and system for building a compact model for large vocabulary continuous speech recognition (LVCSR) system

A system for speech recognition comprising:

• an acoustic model comprising:
• a first plurality of codebooks, each codebook of the first plurality of codebooks being built based upon a mean sub-vector set of a mean vector set, the mean vector set having a plurality of dimensions, the mean vector set including a mean vector of one of a set of N Gaussians that represents state observation probability distributions of previously trained continuous density hidden Markov models (CDHMMs), wherein the mean vector contributes only a sub-vector of the mean vector to the mean sub-vector set, and wherein a set of all dimensions of the mean sub-vector set includes only a subset of the plurality of dimensions of the mean vector set,
• a second plurality of codebooks, each codebook of the second plurality of codebooks being built based upon a variance sub-vector set of a variance vector set, the variance vector set having a plurality of dimensions, the variance vector set including a variance vector of one of a set of N Gaussians that represents state observation probability distributions of previously trained continuous density hidden Markov models (CDHMMs), wherein the variance vector contributes only a sub-vector of the variance vector to the variance sub-vector set, and wherein a set of all dimensions of the variance sub-vector set includes only a subset of the plurality of dimensions of the variance vector set,
• each codebook being built 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 a respective sub-vector set to a 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 cluster set 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;
• a feature extraction unit to convert an input signal representing an input speech into a set of feature vectors each representing a corresponding frame of the input signal;
• and
• a decoder coupled to the acoustic model and the feature extraction unit, the decoder to provide a set of one or more words corresponding to the input speech based, at least in part, upon the feature vectors and the acoustic model.

A machine-readable medium comprising instructions which, when executed by a machine, cause the machine to perform operations for speech recognition 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 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 cluster set to create a new cluster set, and
• splits a cluster of the new cluster set based upon an average distortion of the cluster of 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.