The key problem to be faced when building a HMM-based continuous speech recogniser is maintaining the balance be- tween model complexity and available training data. For large vocabulary systems requiring cross-word context de- pendent modelling, this is particularly acute since many mmh contexts will never occur in the training data. This paper de- scribes a method of creating a tied-state continuous speech recognition system using a phonetic decision tree. This tree- based clustering is shown to lead to similar recognition per- formance to that obtained using an earlier data-driven ap- proach but to have the additional advantage of providing a mapping for unseen triphones. State-tying is also compared with traditional model-based tying and shown to be clearly superior. Experimental resul...

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