A discriminative splitting criterion for phonetic decision trees
Simon Wiesler, Georg Heigold, Markus Nußbaum-Thom, Ralf Schlüter, Hermann Ney · 2010
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.