The invention independently vector-quantizes the spectrum representing the static feature of speech on the frequency axis and the variation pattern of the spectrum on the time axis. The resultant pair of label trains are evaluated, based on the knowledge that there is a small correlation between them, by the equation: P(La, Lc?W) = P(La, Lc?I,W)P(I?W) I = P(La(1)?Ma(i1)P(Lc(1)?Mc(i1)) I P(Bi1,i2?Ma(i1),?Mc(i1)) P(La(2)?Ma(i2))P(Lc(2)?M(i2)) P(Bi2,i3 ?Ma(i2), Mc(i2) ...La(T)?Ma(iT))P(Lc(T)?Mc(iT)) P(BiT, iT+1?Ma(it), Mc(iT)) wherein W designated a Markov model representing a word; I = i1, I2, I3, ... iT, a state train; Ma and Mc, Markov models by label corresponding to the spectrum and the spectrum variation, respectively; and B , a transition from the state i to the scale j. P(La, Lc?W) is calculated for each Markov model W representing a word and W giving the maximum value for it is determined as the recognition result.
VOICE RECOGNITION APPARATUS