EUROSPEECH '97
5th European Conference on Speech Communication and Technology

Rhodes, Greece
September 22-25, 1997


Clustering Beyond Phoneme Contexts for Speech Recognition

Clark Z. Lee, Douglas O'Shaughnessy

INRS-Telecommunications, Verdun, Quebec, Canada

The clustering of using decision trees is generalized to take into account high-level knowledge sources to better model the co- articulation effects in large vocabulary continuous speech recognition. VQ models are used to reduce the computational cost in constructing decision trees. The search algorithm is designed such that it can provide a general type of information for decision trees without compromising the speed. Experiments with a 30k-word dictionary on the WSJ task show that the word error rate can be reduced by considering additional knowledge sources. use much more complex acoustic-phonetic models without compromising the speed in our system. Experiments on the Wall Street Journal task show that it may increase the recognition accuracy to use deci- sion trees with additional knowledge sources.

Full Paper

Bibliographic reference.  Lee, Clark Z. / O'Shaughnessy, Douglas (1997): "Clustering beyond phoneme contexts for speech recognition", In EUROSPEECH-1997, 19-22.