The general use of tying in phoneme-based HMM speech recognisers

A method of manipulating sets of hidden Markov models (HMMs) by applying various kinds of parameter tying operations is described, the aim being to synthesize compact and robust context dependent models. The method is illustrated via an experiment to build a set of generalized triphone models for the TIMIT database in which triphones are constructed by joining together left and right dependent biphones. Although simple, the method results in good performance and avoids the need to train large numbers of triphones. The use of tying to increase model robustness is also investigated. Tying the center states within triphones of the same phoneme class and tying variances within states is beneficial, but larger-scale tying of variances leads to degraded performance