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Summary: JOINT PRONUNCIATION MODELLING OF NON-NATIVE SPEAKERS
USING DATA-DRIVEN METHODS
Ingunn Amdal, Filipp Korkmazskiy and Arun C. Surendran
Multimedia Communications Research Laboratory
Bell Labs, Lucent Technologies
Murray Hill, NJ 07974, USA
amdal@tele.ntnu.no, yelena,acs @research.bell-labs.com
ABSTRACT
Modelling non-native speakers with different mother tongues
is a difficult task for automatic speech recognition due to the large
variation among speakers. One possibility for jointly modelling
all speakers is to use the same speaker independent acoustic mod-
els and a joint lexicon to capture the variation.
We have modified the reference lexicon using pronunciation
rules that are derived in a totally data-driven manner from a set of
adaptation data using the reference recognizer and the reference
lexicon. Deriving common rules for such diverse sources simul-
taneously is difficult. The challenge is to combine these rules to a
common set without increasing the confusability.
In this paper we compare several methods of combining the
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