Comparison of acoustic model adaptation techniques on non-native speech

Abstract

The performance of speech recognition systems is consistently poor on non-native speech. The challenge for non-native speech recognition is to maximize the recognition performance with a small amount of available non-native data. We report on acoustic modeling adaptation for the recognition of non-native speech. Using non-native data from German speakers, we investigate how bilingual models, speaker adaptation, acoustic model interpolation and polyphone decision tree specialization methods can help to improve the recognizer performance. Results obtained from the experiments demonstrate the feasibility of these methods.