Method and apparatus for speech recognition using optimized partial mixture tying of HMM state functions

In a speech recognition system for responding to signals representative of digital speech, a method for developing models for subsets of speech comprising the steps of:

• selecting a multi-state model with state probability functions, said probability functions being of a general form with initially undetermined parameters;
• creating an individual instance of a model for each subunit of speech to be processed;
• clustering states based on their acoustic similarity;
• creating a plurality of cluster codebooks, one codebook for each cluster;
• said cluster codebooks consisting of probability density functions that are shared by each cluster's states;
• estimating the probability densities of each cluster codebook and the parameters of the probability equations in each cluster.

A method for recognizing speech using a computer comprising the steps of:

• selecting a multi-state model with state probability functions, said probability functions being of a general form with initially undetermined parameters;
• creating an individual instance of a model for each subunit of speech to be processed;
• training the models with a training algorithm that determines parameters for the models that best fit a set of features extracted from known speech samples;
• clustering states into a predetermined number of clusters of states wherein the states in each said cluster have probability functions that can be well represented by a shared group of simple probability functions;
• developing a cluster codebook of simple probability functions for each cluster and storing said cluster codebooks;
• storing for each state an identifier for a cluster codebook and an array of weighting factors;
• extracting features from a speech sample to be recognized;
• and
• using said state probability functions and said cluster codebooks to determine a most probable state sequence for said speech sample.

In a speech recognition system for responding to signals representative of digital speech, a method for developing models for subsets of speech comprising the steps of:

• selecting a multi-state model with state probability functions, said probability functions being of a general form with initially undetermined parameters;
• creating an individual instance of a model for each subunit of speech to be processed;
• clustering states based on their acoustic similarities;
• creating a plurality of cluster codebooks, one cluster codebook for each cluster;
• each codebook comprising a group of shared probability functions;
• and
• re-estimating the probability densities of each cluster codebook and the parameters of the probability equations for each state in each cluster.

A speech recognizer, comprising:

• a computer;
• storage means;
• a set of models for subunits of speech stored in the storage means;
• a feature extractor in the computer for extracting feature data capable of being processed by said computer from a speech signal;
• training means in the computer for training the models using features from identified samples of speech data and for producing a master codebook of probability density functions for use by the models;
• clustering means in the computer for identifying clusters of states that share subsets of the probability density functions in the codebooks;
• splitting and pruning means in the computer for producing cluster codebooks by splitting the master codebook into subsets of probability densities shared by clustered states;
• re-estimating means for retraining the models for the states in the clusters and for recalculating the probability densities in each cluster codebook;
• recognizing means for matching features from unidentified speech data to the models to produce a most likely path through the models where the path defines the most likely subunits and words in the speech data.