Speaker dependent connected speech recognition via phonetic Markov models

In this paper, a method for speaker dependent connected speech recognition based on phonemic units is described. In this recognition system, each phoneme is characterized by a very simple 3-state Hidden Markov Model (HMM) which is trained on connected speech by a Viterbi algorithm. Each state has associated with it a continuous (Gaussian) or discrete probability density function (pdf). With the phonemic models so obtained, the recognition is then performed either directly at word level (by the reconstruction of reference words from the models of the constituting phonemes) or via a phonemic labelling. Good results are obtained as well with a German ten digit vocabulary (20 phonemes) as with a French 80 word vocabulary (36 phonemes).