Phonetically guided clustering for isolated word recognition

A variant of the Markov source modelling of entire words based on automatically determined subword units is described. Each word of the vocabulary is modelled as a linear sequence of phoneme segments given by a phonetic transcription. For every phoneme a minimum and maximum duration are to be specified. Matching an utterance to the models must be performed within these absolute durational constraints. This is achieved by a dynamic programming time alignment different from the conventional ones. The acoustic emission is defined by means of phonetically labelled prototype vectors. The parameters of the models are automatically trained by an iterative procedure similar to the Viterbi algorithm. The method is applied to speaker-dependent and independent recognition of the German digits (telephone speech).