Method, apparatus and system for building a compact language model for large vocabulary continous speech recognition (lvcsr) system

A method comprising:

• clustering a set of probabilistic values associated with each data class of a plurality of data classes of an N-gram language model into a plurality of clusters using a modified K-means clustering process which dynamically adjusts the size and centroid of each respective cluster during each iteration in the modified K-means clustering process until one or more predetermined conditions are satisfied, the clustering including:
• initializing the plurality of clusters such that all clusters have equivalent areas;
• setting the centroid of each resultant cluster such that the area of each respective cluster is approximately equally split based on the corresponding centroid;
• and
• performing a set of operations iteratively until one or more predetermined conditions are satisfied, including:
• computing a total deviation of the clusters;
• replacing the edge of each cluster by an arithmetic average of the nearest two centroids;
• and
• setting a new centroid for each resultant segment.

A system comprising:

• 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;
• an acoustic model comprising a set of phonetic statistical models each representing a distinct phonetic unit;
• a language model including:
• a plurality of codebooks each being built based upon a corresponding class of probabilistic values that represent conditional probabilities of a set of words in the language model, each codebook including a plurality of codewords each representing one or more corresponding probabilistic values, wherein each codebook of the plurality of codebooks is built using a modified K-means clustering process which dynamically adjusts the size and the centroid of each cluster during each iteration in the modified K-means clustering process until a total deviation of the clusters is optimized based upon one or more optimization criteria;
• and
• a decoder coupled to the feature extraction unit, the acoustic model, and the language model, the decoder to recognize the input speech based, at least in part, upon the feature vectors, the acoustic model, and the language model.

A machine-readable medium comprising instructions which, when executed by a machine, cause the machine to perform operations including:

• classifying a set of probabilistic attributes in an N-gram language model into a plurality of classes;
• clustering each resultant class into a plurality of segments to build a codebook for the respective class using a modified K-means clustering process which dynamically adjusts the size and centroid of each segment during each iteration in the modified K-means clustering process;
• and
• representing a probabilistic attribute in each class by the centroid of the corresponding segment to which the respective probabilistic attribute belongs.