22. The system of claim 21, wherein said means for updating output distributions comprises means for determining a probability of observing a feature vector x.sub.t at a state j according to an equation: ##EQU14## where: "x.sub.t " is one of one or more feature vectors which are grouped into one or more clusters; "s.sub.j " is a state of said hidden markov model; "k" is the total number of said feature vector clusters; "g.sub.k.sup.1 " is one of said one or more clusters; "g.sub.k.sup.2 " is one of said one or more clusters; and ".alpha." is a weighting term.
21. The system of claim 17, wherein said updating means comprises: (1) means for determining a most probable path through said hidden Markov model for each training observation sequence; (2) means for updating said model parameters based on relative frequencies of state transitions and on observed values emitted during said state transitions, comprising: (I) means for updating said one or more output probability distributions; (II) means for updating said one or more mixture coefficients; and (III) means for updating state transition probabilities.