Year: 2010
Type: conference-paper
Abstract: 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.
Source: Interspeech (USB)
Institution: RWTH Aachen University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4473315