Phoneme classification for real time speech recognition of Italian

The development of large dictionary speech recognition systems requires the use of techniques aimed at limiting the search of the correct word to a subset of the vocabulary as small as possible. An approach to this problem is to create classes of equivalence among words by means of a phoneme classification. We investigate methods based on the definition of a similarity measure of Hidden Markov Models of phonemes, and on the automatic identification of broad phonetic classes via clustering algorithms. We discuss the obtained classifications, and their use in a real time speech recognition system for a 3000-word dictionary for Italian; results are compared to those achieved by knowledge based classifications.