Reducing out-of-vocabulary in morphology to improve the accuracy in Arabic dialects speech recognition

This thesis has two aims: developing resources for Arabic dialects and improving the speech recognition of Arabic dialects. Two important components are considered: Pronunciation Dictionary (PD) and Language Model (LM). Six parts are involved, which relate to finding and evaluating dialects resources and improving the performance of systems for the speech recognition of dialects. Three resources are built and evaluated: one tool and two corpora. The methodology that was used for building the multi-dialect morphology analyser involves the proposal and evaluation of linguistic and statistic bases. We obtained an overall accuracy of 94%. The dialect text corpora have four sub-dialects, with more than 50 million tokens. The multidialect speech corpora have 32 speech hours, which were collected from 52 participants. The resultant speech corpora have more than 67,000 speech files. The main objective is improvement in the PDs and LMs of Arabic dialects. The use of incremental methodology made it possible to check orthography and phonology rules incrementally. We were able to distinguish the rules that positively affected the PDs. The Word Error Rate (WER) improved by an accuracy of 5.3% in MSA and 5% in Levantine. Three levels of morphemes were used to improve the LMs of dialects: stem, prefix+stem and stem+suffix. We checked the three forms using two different types of LMs. Eighteen experiments are carried out on MSA, Gulf dialect and Egyptian dialect, all of which yielded positive results, showing that WERs were reduced by 0.5% to 6.8%.