Patent Document ID: 4348553
Application ID: 06165466

Base Claim:
1. A speech recognition system, comprising: an elementary recognizer for classifying the elementary segments of an observed speech pattern as they are received, said elementary recognizer including correlation means for producing at an output node of said elementary recognizer a score of correlation of said elementary segments with stored spectral speech patterns; and a plurality of local decision modules each connected to said output node for receiving said score of correlation; said plurality of local decision modules being connected at node points in a network wherein different network paths through the nodes and their corresponding local decisions modules represent an accumulation of speech segments constituting different pronunciations of said speech pattern, the input of each said local decision module connected to said correlation means to receive the measures of correlation; each local decision module specializing in a particular network node and including, means for determining the probability of how well the input segment of speech matches the particular sound segments associated with a given node, means for receiving from the other local decision modules the prior correlation scores of all preceding sound segments, means for selecting the locally optimum time warping of each segment of speech which are input from other local decision modules, and accumulator memory means for providing an accumulated correlation score for any one path in the network of local decision modules, said path representing an accumulation of segments or parts of a word or sound; whereby the accumulated correlation score represents the most probable pronunciation of said speech pattern and the best recognition match derived from all the possible paths in the network of local decision modules.

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Claim 7:
7. A system as recited in claim 1, wherein said accumulator memory means in each local decision module stores said logarithmic probabilities of the current observation in accordance with the following fundamental equation for the dynamic programming solution to the maximum log probability with nonlinear time warping of a hidden Markov process; ##EQU6## wherein .gamma.(t,j) represents the log probability for the best partial path which winds up at state j in the word prototype at time t thereby presenting the best path to arrive at a given node including all observations; t=1, 2, 3, . . . , T with all the local decision modules computing with the same value of t during a given cycle in the computation; .gamma.(j,t) represents the present accumulated score up to and including the present score along the best possible path in the network; the symbol b[j,p(t)] represents the correlation score for a single elementary pattern segment at a particular time where an input segment (s,t) has a stored pattern (p,i,j); the term a(i,j) is the probability of going from position i to position j in the prototype for a single position step in the observed pattern; whereby the different values of i correspond to different positions within the prototype of a given word, and selection of a different value of i from time (t-1) to be connected to state j at time t represents the selection of a locally optimum dynamic time warping.