Short vowels in Arabic are normally omitted in written text which leads to ambiguity in the pronunciation. This is even more pronounced for dialectal Arabic where a single word can be pronounced quite differently based on the speaker's nationality, level of education, social class and religion. In this paper we focus on pronunciation modeling for Iraqi-Arabic speech. We introduce multiple pronunciations into the Iraqi speech recognition lexicon, and compare the performance, when weights computed via forced alignment are assigned to the different pronunciations of a word. Incorporating multiple pronunciations improved recognition accuracy compared to a single pronunciation baseline and introducing pronunciation weights further improved performance. Using these techniques an absolute reduction in word-error-rate of 2.4% was obtained compared to the baseline system.
['Hassan Al-Haj', 'Roger Hsiao', 'Ian R. Lane', 'Alan W. Black', 'Alex Waibel']
Pronunciation modeling for dialectal arabic speech recognition