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.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Simon Wiesler, Georg Heigold, Markus Nußbaum-Thom, Ralf Schlüter, Hermann Ney
• Keywords: Conventional Phonetic Decision, INTERSPEECH 2010, Phonetic Decision, Signal Processing, Speech Recognition