Extending the vocabulary of a speaker independent recognition system

The authors discuss the extension and adaptation of a speaker-independent, small-vocabulary, isolated word recognition system based on tied density hidden Markov models. In the proposed approach, the density functions are trained from a basic set of words using acoustic segmentation, position-dependent segment labeling, and clustering of the segment specific densities. Then the parameters of the word models are estimated by means of a Viterbi update procedure. With a given set of densities the Viterbi update can also be used to generate models for words not included in the basic set. The dependency between the recognition performance and the amount of reference data both for speaker-independent and speaker-dependent experiments is examined in detail. The authors compare different algorithms to avoid zero probabilities in the word models due to insufficient data