6. A method of training a speech model as set forth in claim 5 wherein said hidden Markov model consists of a fenonic hidden Markov model.
5. A method of speech recognition comprising the steps of: retaining fenonic hidden Markov models for each speech transformation candidate, the hidden Markov models comprising allophones of one or more subwords of the candidate, by assigning to each allophone an N-gram relation (N=an integer greater than or equal to 2) with the speech transformation candidates of other preceding subwords in the word; retaining label sequences for each speech transformation candidate on which the subwords of a word are transformed as speech; retaining a plurality of candidate words to be recognized; analyzing a word inputted as speech for its features and obtaining a label sequence or feature vector sequence corresponding to the word concerned; applying fenonic hidden Markov models to each speech transformation candidate in correspondence to said candidate words and on the basis of said N-gram relation; concatenating each fenonic hidden Markov model for each of these speech transformation candidates in parallel among the subwords to compose a speech model; determining the probability of a speech model composed with regard to each candidate word to output the label sequence or feature vector sequence of said word inputted as speech, and outputting a candidate word corresponding to the speech model of the highest probability as a result of recognition.