Triphone tying techniques combining a-priori rules and data driven methods

Tying of Hidden Markov Model states is an important issue for the use of triphones as modeling units in automatic speech recognition systems. This paper studies the application of a–priori rules for tying in combination with data driven methods. The baseline method features a combination of a–priori rules that reduce the theoretical number of units by an oder of magnitude and a simple back–off tying. Back–off tying is based on the frequency of units appearing in the training material. The use of the a–priori rules has practical advantages especially for the implementation of continuous phoneme recognition. This method is compared to the widely used decision tree based clustering that makes no use of a–priori rules. A third method is proposed that combines a–priori rules with decision tree based clustering. Experiments on telephone data show that the combined method outperforms both other methods preserving the advantages of applying a–priori rules.