Unsupervised Acoustic Model Adaptation for Multi-Origin Non Native ASR

摘要

To date, the performance of speech and language recognition systems is poor on non-native speech. The challenge for non-native speech recognition is to maximize the accuracy of a speech recognition system when only a small amount of non-native data is available. We report on the acoustic model adaptation for improving the recognition of non-native speech in English, French and Vietnamese, spoken by speakers of different origins. Using online unsupervised adaptation acoustic modeling without any additional data for adapting purposes, we investigate how an unsupervised multilingual acoustic model interpolation method can help to improve the phone accuracy of the system. Results improvement of 7% of absolute phone level accuracy (PLA) obtained from the experiments demonstrate the feasibility of the method.