A discriminative splitting criterion for phonetic decision trees

Phonetic decision trees are a key concept in acoustic modeling for large vocabulary continuous speech recognition. Although discriminative training has become a major line of research in speech recognition and all state-of-the-art acoustic models are trained discriminatively, the conventional phonetic decision tree approach still relies on the maximum likelihood principle. In this paper we develop a splitting criterion based on the minimization of the classification error. An improvement of more than 10 % relative over a discriminatively trained baseline system on the Wall Street Journal corpus suggests that the proposed approach is promising. Index Terms: discriminative training, phonetic decision trees, state tying, new paradigms 1.