22. A speech processing method for processing speech including a succession of utterances spoken in any of continuous and discrete form 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 spoken in any of continuous and discrete form, where w is an integer greater than 1, each of said frame data sets being representative of successive acoustic segments of utterances spoken in any of continuous and discrete form 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&lt;w, each of said reduced frame data sets being related to an associated plurality of said frame data sets and including j values 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.
22. A speech processing method for processing speech including a succession of utterances spoken in any of continuous and discrete form 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 spoken in any of continuous and discrete form, where w is an integer greater than 1, each of said frame data sets being representative of successive acoustic segments of utterances spoken in any of continuous and discrete form 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&lt;w, each of said reduced frame data sets being related to an associated plurality of said frame data sets and including j values 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. 26. A method according to claim 22 wherein said storing step includes the substep of generating for each of said word models, r node data vectors f.sub.1, . . . , f.sub.r, where r is less than or equal to s, each of said node data vectors being representative of a characteristic related to the occurrence of a selected one of acoustic segments from each word of a set of words associated with said cluster data sets in said acoustic segment of said utterances spoken in any of continuous and discrete form, and wherein said evaluating step includes the substep of determining said cluster score S.sub.Y in accordance with: ##EQU7## wherein Y.sub.i are ones of Y.sub.1, Y.sub.2, . . . , Y.sub.s, and f.sub.i are said node data vectors f.sub.1, . . . , f.sub.r for corresponding ones of said word models.