Automatic pronunciation error detection of nonnative Arabic Speech

Abstract

Computer assisted language learning (CALL) and, more specifically, computer assisted pronunciation training (CAPT) have received considerable attention in recent years. CAPT allows continuous feedback to the learner without requiring the sole attention of the teacher; it facilitates self study and encourages interactive use of the language in preference to rote learning. One of the important processes in CAPT system is error detection, which locates the errors in the utterance. Although Arabic is currently one of the most widely spoken languages in the world, there has been relatively little research about detection of the pronunciation error by nonnative speakers compared to the other languages. This research is concerned with detecting pronunciation errors of nonnative Arabic speakers from Pakistan and India. All the sounds in this study were taken from King Saud University (KSU) Arabic Speech Database. By analyzing the speech of the Pakistani and Indian speakers in KSU database we found that five phonemes were often mispronounced by nonnative speakers, hence this research will concentrate on pronunciation errors in these five phonemes. The system was built with native and nonnative speakers, and tested with nonnative only. For each phoneme, the Goodness of Pronunciation (GOP) was calculated and compared with a threshold to decide if the phoneme was pronounced correctly or not. The result showed that GOP gave high accuracy, where the scoring accuracy was very good to excellent from 87% to 100%, and the false rejection was zero to less than 10%. This machine judgment is compared with human judgment and the comparison shows excellent agreement between them.