Language learning based on non-native speech recognition

This work presents methods of assessing non-native speech to aid computer-assisted pronunciation teaching. These methods are based on automatic speech recognition (ASR) techniques using Hidden Markov Models. Conn-dence scores at the phoneme level are calculated to provide detailed information about the pronunciation quality of a foreign language student. Experimental results are given based on both artiicial data and a database of non-native speech, the latter being recorded speciically for this purpose. The presented results demonstrate the metrics' capability to locate and assess mispronunciations at the phoneme level.