Rapid phonetic transcription using everyday life natural Chat Alphabet orthography for dialectal Arabic speech recognition

We propose the Arabic Chat Alphabet (ACA) as naturally written in everyday life for dialectal Arabic speech transcription. Our assumption is that ACA is a natural language that includes short vowels that are missing in traditional Arabic orthography. Furthermore, ACA transcriptions can be rapidly prepared. Egyptian Colloquial Arabic was chosen as a typical dialect. Two speech recognition baselines were built: phonemic and graphemic. Original transcriptions were re-written in ACA by different transcribers. Ambiguous ACA sequences were handled by automatically generating all possible variants. ACA variations across transcribers were modeled by phonemes normalization and merging. Results show that the ACA-based approach outperforms the graphemic baseline while it performs as accurate as the phoneme-based baseline with a slight increase in WER.