Speech corpus for Medina dialect

Abstract

Automatic Speech Recognition (ASR) has standard rules which must be followed and considered carefully. Some difficulties that lead to less ASR performance is variations in pronunciation and small words misrecognition. Arabic ASR faces some challenges like difficulty in obtaining corpora for spoken dialects. Obtaining a wide range of diacritized text as well as the enormous number of word forms is considered a major challenge due to the Arabic language morphology richness and its’ letters capability to be written without diacritics. Although Arabic is one of the most popular languages, Arabic ASR systems are still rare compared with other languages. As ASR systems depend primarily on speech corpuses, Arabic ASR systems requires specific-dialect speech corpuses. Such speech corpuses are still deficient, costly, nor sometimes exists. In this research, we contribute to overcome the lack of speech recognition and misunderstanding for one of the most famous dialects in Saudi Arabia, Medina. We created a brand-new corpus “Haneen Corpus“, which consists of 70,364 tokens that have been uttered using Medina dialect, and constructed a dictionary using 64 phonemes to analyse the correct pronunciation of words. Our Medina Dialect ASR System exploited Hidden Markov Models (HMM) that achieved 92.09 % speech recognition accuracy.

Keywords

• Machine Learning (ML)
• Natural Language Processing (NLP)
• Automatic Speech Recognition
• Arabic ASR Speech Corpus
• Arabic Dialects
• CMU Sphinx