Investigating Morphological Decomposition for Transcription of Arabic Broadcast News and Broadcast Conversation Data

One of the challenges of Arabic speech recognition is to deal with the huge lexical variety. Morphological decomposition has been proposed to address this problem by increasing lexical coverage, thereby reducing errors that are due to words that are unknown to the system. In our previous attempts to develop an Arabic speech-to-text (STT) transcription system with morphological decomposition, an increase in word error rate of about 2% absolute was observed relative to a comparable word based system. Based on an error analysis and a comparison of our approach with that of other sites, two modifications were made. The first modification was to not decompose the most frequent words; and the second to not decompose the prefix 'A1' for words starting with a solar consonant since due to assimilation with the following consonant, deletion of the prefix was one of the most frequent errors. Comparable recognition performance was achieved using word-based and morphologically decomposed language models, and since the errors made by the systems are different, combining the two gave a performance gain.