Large-vocabulary continuous speech prefiltering and processing system

A prefiltering system for processing continuous speech, said speech including a succession of utterances, comprising:

• A. cluster data storage means for storing a plurality of M cluster data sets, C.sub.1, . . . , C.sub.M, where M is an integer greater than 1, each of said cluster data sets including data representative of a plurality of word models;
• B. frame data means for generating succession of w frame data sets v.sub.t, v.sub.t +1, . . . v.sub.t +w-1, beginning at a frame start time t during said succession of utterances, where w is an integer greater than 1, said succession of frame data sets being representative of a corresponding succession of temporal segments of said utterances, each of said frame data sets including k values representative of different frame parameters, where k.gtoreq.1;
• C. data reduction means selectively operable on said w frame data sets for generating s reduced frame data sets where s<w, each of said reduced frame data sets Y.sub.1, Y.sub.2, . . . , Y.sub.3, being related to an associated plurality of said frame data sets and including j values representative of different reduced frame data set parameters;
• D. scoring means for evaluating each of said reduced frame data sets against a succession of said cluster data sets to generate a cluster score S.sub.Y for each of said cluster data sets;
• E. selectively operable identifying means for identifying each of said word models of said cluster data sets having a cluster score bearing a predetermined relation to at least one threshold score T, said identified word models defining a candidate word list;
• F. control means for determining said frame start times t, where successive start times t are spaced apart arbitrarily, said frame start times being independent of identification of an initial anchor;
• and
• G. means for generating a signal representative of said candidate word list for preselected ones of said frame start times t determined by said control means.

A continuous speech processing method for processing speech including a succession of utterances comprising the steps of:

• A. storing a plurality of M cluster data sets, C.sub.1, . . . , C.sub.M, where M is an integer greater than 1, each of said cluster data sets including data representative of a plurality of word models;
• B. generating a succession of w frame data sets v.sub.t, v.sub.t +1, . . . v.sub.t +w-1, beginning at a frame start time t during said succession of utterances, where w is an integer greater than 1, each of said frame data sets being representative of successive acoustic segments of utterances for a frame period, each of said frame data sets including k values representative of different frame parameters where k.gtoreq.1;
• C. reducing w of said frame data sets to generate s reduced frame data sets Y.sub.1, Y.sub.2, . . . , Y.sub.3, where s<w, each of said reduced frame data sets being related to an associated plurality of said frame data sets and including j related to the k values of said associated frame data sets, where j.ltoreq.k;
• D. evaluating said reduced frame data sets with a succession of said cluster data sets to generate a cluster score S.sub.Y for each of said cluster data sets;
• E. identifying each of said word models having a cluster score bearing a predetermined relation to at least one threshold score T, said identified word models defining a word list;
• F. determining said frame start times, t, where successive start times t are identified at arbitrarily selected intervals, said frame start times being independent of identification of an initial anchor;
• and
• G. generating a signal representative of said candidate word list for selected ones of said frame start times determined in step F.

A prefiltering method for processing continuous speech, said speech including a succession of utterances, comprising the steps of:

• A. storing a plurality of word models;
• B. identifying a succession of temporal segments of said utterances;
• C. operating on ones of said segments and selectively identifying a subset of said plurality of word models meeting predetermined criteria, said subset defining a list of candidate words;
• D. determining arbitrarily selected frame start times t during said utterances, said frame start times being independent of identification of an initial anchor;
• and
• E. generating a signal representative of said list of candidate words for selected ones of said frame start times t determined in step D.