Morphemes in Arabic Automatic Speech Recognition

Arabic is a morphologically rich language which rarely displays diacritics. These two features of the language pose challenges when building Automatic Speech Recognition (ASR) systems. Morphological complexity leads to many possible combinations of stems and affixes to form words, and produces texts with high Out Of Vocabulary (OOV) rates. In addition, texts rarely display diacritics which informs the reader about short vowels, geminates, and nunnations (word ending /n/). A lack of diacritics means that 30 % of textual information is missing, causing ambiguities in lexical and language modeling when attempting to model pronunciations, and the context of a particular pronunciation. Intuitively, from an English centric view, the phrase thwrtr wrt n thwrt with ‘morphological decomposition ’ is realized as, th wrtr wrt n th wrt. Including ‘diacritics ’ produces, the writer wrote in the writ. Thus our investigations in this thesis are twofold. Firstly, we show the benefits and interactions between modeling all classes of diacritics (short vowels, geminates