Speech recognition system and method employing data compression

A data compression system for a computerized speech recognizer having a plurality of hidden Markov models representing acoustic utterances, comprising:

• a receiver that receives a plurality of spoken training utterances;
• means for converting the spoken training utterances into an output probability table having a predetermined number of codewords, each codeword representing an acoustic feature of the spoken training utterances and each codeword being associated with a probability value for each hidden Markov model, each probability value reflecting a probability that the acoustic utterance represented by the hidden Markov model will be spoken using the acoustic feature represented by the associated codeword;
• a memory that stores the output probability table;
• and
• a data processor that compresses at least some of the probability values associated with a selected one of the codewords based on others of the probability values associated with the selected codeword, thereby compressing the stored output probability table.

A computer storage medium encoded with a data structure for use by a computerized speech recognizer having a plurality of hidden Markov models representing acoustic utterances, the data structure comprising:

• a predetermined number of codewords, each codeword representing an acoustic feature;
• and
• a probability value associated with each codeword for each hidden Markov model, each probability value reflecting a probability that the acoustic utterance represented by the associated hidden Markov model will be spoken using the acoustic feature represented by the associated codeword, at least some of the probability values associated with a selected codeword being in a compressed form that is based on others of the probability values associated with the selected codeword, the compressed form being such that the probability values associated with the selected codeword can be decompressed by the computerized speech recognizer without decompressing the probability values associated with codewords other than the selected codeword;
• computer instructions stored on the computer storage medium, the computer instructions recognizing a sound spoken by a user, the sound spoken being represented by an input speech signal that includes a plurality of input frames, each input frame being associated with one of the codewords, the instructions including:
• instructions for decompressing only those probability values associated with the codewords that are identical to the codewords of the input frames without decompressing probability values associated with codewords not identical to the codewords of the input frames;
• instructions for computing a recognition score for each of the hidden Markov models using the decompressed probability values;
• and
• instructions for determining which hidden Markov model results in a highest recognition score and thereby recognizing the acoustic utterance represented by the hidden Markov model with the highest recognition score as being the spoken sound represented by the input speech signal.

A data compression method for a computerized speech recognizer having a plurality of hidden Markov models representing acoustic utterances, comprising:

• receiving a plurality of spoken training utterances;
• and
• convening the spoken training utterances into an output probability table, the convening step including:
• creating a predetermined number of codewords based on the spoken training utterance, each codeword representing an acoustic feature of the spoken training utterances;
• creating the hidden Markov models based on the spoken training utterances, each hidden Markov model being created by steps that include associating a probability value with each codeword, each probability value reflecting a probability that the acoustic utterance represented by the hidden Markov model will be spoken using the acoustic feature represented by the associated codeword;
• and
• compressing the probability values for each codeword separately from the probability values for other codewords, such that a selected probability value associated with a selected codeword can be decompressed without decompressing the probability values associated with a different, non-selected codeword.

A data compression method for a computerized speech recognizer having a plurality of utterance models, each utterance model representing a speech utterance, each utterance model being associated with a probability value for each of a plurality of acoustic features, each probability value reflecting a probability that the speech utterance represented by the associated utterance model will be spoken using the associated acoustic feature, each acoustic utterance being represented by a codeword, the method comprising:

• retrieving the probability values for all the utterance models for a selected codeword;
• encoding the retrieved probability values independently from the probability values for codewords other than the selected codeword, such that the encoded probability values for the selected codeword can be decoded without decoding the probability values associated with a different, non-selected codeword;
• storing the encoded probability values;
• and
• recognizing an input speech utterance using the stored encoded probability values.

A speech recognition method for recognizing an input speech utterance spoken by a user, the method employing a computerized speech recognizer having a plurality of utterance models representing speech utterances, the computerized speech recognizer including a compressed output probability table having, for each utterance model, a probability value for each codeword in a set of codewords representing acoustic features, each probability value reflecting a probability that the speech utterance represented by the utterance model for the probability value will be spoken using the acoustic feature represented by the codeword for the probability value, at least some of the probability values for each codeword being compressed, the method comprising:

• inputting a speech signal representing the input speech utterance, the speech signal including a sequence of input frames, each input frame representing a sound of the input speech utterance;
• for each input frame:
• determining which of the codewords most closely matches the sound represented by the input frame;
• decompressing only those probability values for the codeword that is determined to most closely match the sound represented by the input frame without decompressing the probability values for the codewords that do not most closely match the sound represented by the input frame;
• and
• computing a recognition score for each of the utterance models using the decompressed probability values, the recognition score for each of the utterance models being updated with each input frame;
• and
• determining which utterance model results in a highest recognition score based on the computing step;
• the speech utterance represented by the utterance model with the highest recognition score being recognized as the input speech utterance.