Language learning based on non-native speech recognition
Silke M. Witt, Steve J. Young · 1997
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. Confidence 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 artificial data and a database of non-native speech, the latter being recorded specifically for this purpose. The presented results demonstrate the metrics' capability to locate and assess mispronunciations at the phoneme level. 1. INTRODUCTION With the rapid progress of speech recognition, current research challenges are to improve non-standard speech recognition such as for dialects or non-native speech. This project applies non-native speech recognition to pronunciation teaching. So far computer-assisted language teaching was restricted to "record and play", because without speech proces...