Adaptation Methods For Non-Native Speech

LVCSR performance is consistently poor on low-proficiency non-native speech. While gains from speaker adaptation can often bring recognizer performance on high-proficiency non-native speakers close to that seen for native speakers [12], recognition for lower-proficiency speakers remains low even after individual speaker adaptation [2]. The challenge for accent adaptation is to maximize recognizer performance without collecting large amounts of acoustic data for each native-language/target-language pair. In this paper, we focus on adaptation for lower-proficiency speakers, exploring how acoustic data from up to 15 adaptation speakers can be put to its most effective use