Arabic Language Learning Assisted by Computer, based on Automatic Speech Recognition
This work addresses Arabic language learning for students, but it is incremental as it applies an existing ASR method to a new language domain.
The authors tackled the problem of Arabic language learning by developing a Computer Assisted Language Learning system using an HMM-based Automatic Speech Recognition tool (CMU Sphinx3) on a six-hour corpus from nine speakers, achieving encouraging results that suggest potential for further improvements.
This work consists of creating a system of the Computer Assisted Language Learning (CALL) based on a system of Automatic Speech Recognition (ASR) for the Arabic language using the tool CMU Sphinx3 [1], based on the approach of HMM. To this work, we have constructed a corpus of six hours of speech recordings with a number of nine speakers. we find in the robustness to noise a grounds for the choice of the HMM approach [2]. the results achieved are encouraging since our corpus is made by only nine speakers, but they are always reasons that open the door for other improvement works.