Investigating the adaptation of Arabic speech recognition systems to foreign accented speakers

This paper investigates Arabic speech recognition systems adaptation to foreign accented speakers. This adaptation scheme is accomplished by using the Maximum Likelihood Linear Regression (MLLR), Maximum a posteriori (MAP), and, then, combination of MLLR and MAP techniques. The HTK toolkit for speech recognition is used throughout all experiments. The systems were evaluated using both word and phoneme levels. The LDC West Point Modern Standard Arabic (MSA) corpus is used throughout the experiments. Results show that particular Arabic Phonemes such as pharyngeal and emphatic consonants, that are hard to pronounce for non-native speakers, benefit from the adaptation process using MLLR and MAP combination. An overall improvement of 7.37% has been obtained.