Improved vocabulary-independent sub-word HMM modelling

The authors describe two techniques for improving the performance of subword recognition on open vocabularies using vocabulary-independent training. The first uses a subtriphone unit called a phonicle to allow triphones which have not been encountered in the training data to be built from contexts which have been sufficiently trained. The second uses linear discriminant analysis to improve discrimination between sound classes. The two techniques have been evaluated for speaker-dependent operation on an open vocabulary task. The recognizer is based on hidden Markov modeling (HMM) using continuous probabilities. The results obtained show that both techniques lead to improved recognition performance