Spanish phone recognition using semicontinuous hidden Markov models

The design of current acoustic-phonetic decoders for a specific language involves the selection of an adequate set of sublexical units and the choice of the mathematical framework for modeling such units. The authors first discuss the choice of sublexical units they made for a Spanish continuous speech decoder. The goal was to build a general, vocabulary-independent, speaker-independent Spanish continuous speech phonetic decoder with only sublexical knowledge. For this purpose, a corpus of 1700 Spanish sentences uttered by ten speakers was used. In addition, different approaches to the Viterbi-based reestimation procedure were considered in the framework of semicontinuous hidden Markov modeling. These simple and less complex computational approaches allow the codebook to be updated in the training phase and make it possible to obtain better decoding results than with the discrete hidden Markov model, mainly in speaker-independent experiments.<>