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.
Bibliographic reference. Wiesler, Simon / Heigold, Georg / Nußbaum-Thom, Markus / Schlüter, Ralf / Ney, Hermann (2010): "A discriminative splitting criterion for phonetic decision trees", In INTERSPEECH-2010, 54-57.