1. A method of speech recognition based on a hidden Markov model in which a word to be recognized is modeled as a chain of states and trained using predefined speech data material, the method comprising: dividing a known vocabulary into a first partial vocabulary of words and a second partial vocabulary of other words, wherein for the first partial vocabulary, only at least one of easily interchangeable words and important words are identified and assigned to the first partial vocabulary and wherein the other words of said known vocabulary are only assigned to the second partial vocabulary and trained using a phoneme-based model; training and transcribing the words of the first partial vocabulary using a whole word model wherein each word of the first partial vocabulary is modeled by a chain of states by dividing each word into a plurality of sections which only apply to the respective word; transcribing the sections of each word of the first partial vocabulary with a word identifier and an index and transcribing the second partial vocabulary using the phoneme-based model, wherein the words are modeled by means of states that correspond to phonemes or parts of phonemes, in order to obtain a corresponding mixed hidden Markov model by storing the first partial vocabulary in the form of word identifiers with indices and the second partial vocabulary in the form of phonetic transcriptions in a single pronunciation lexicon; and storing the mixed hidden Markov model in a single search space, wherein the states of the phoneme-based model correspond to phonemes or parts of phonemes and are used in a plurality of words and the states of the whole word model only apply to the respective word.