5. A recognition system according to claim 1, wherein said memory means includes a memory section for storing the transition probabilities p(k,i,j) and the orthogonal vectors .phi..sub.m (k,s) formed of predetermined eigen vectors contained in correlation matrices R(k,s) of the feature vector sequence X, for each continuous density HMM.
5. A recognition system according to claim 1, wherein said memory means includes a memory section for storing the transition probabilities p(k,i,j) and the orthogonal vectors .phi..sub.m (k,s) formed of predetermined eigen vectors contained in correlation matrices R(k,s) of the feature vector sequence X, for each continuous density HMM. 6. A recognition system according to claim 5, wherein said processing means includes a processing section for obtaining the output probabilities g(k,s) by setting the orthogonal vectors .phi..sub.m (k,s) into a following equation EQ2, and accumulating a sum of each value of g(k,s) and a corresponding logarithmic value of p(k,i,j) for the feature vector sequence X to produce the accumulated value and the accumulation count for each model, and a discriminator for estimating similarity between the feature vector sequence X and each continuous density HMM from the accumulated value and the accumulating count for each model, ##EQU5## where, represents an inner product, and L represents a number of axis of the orthogonal vector set.