Synopsis on Arabic speech recognition

Abstract

With the advancement and increased usage of intelligible smart devices, researchers have an intensified interest in the field of large-vocabulary speaker-independent continuous speech recognition. Although considerable research has been devoted to English speech recognition, less attention has been paid to the Arabic speech recognition. This paper aims to highlight the achievements that have been made during the last several decades of Arabic speech recognition. The paper also discusses speech recognition components such as corpora, phonemes, language models, acoustic models, and performance evaluation. For an empirical evaluation of Arabic speech recognition, the free, off-the-shelf Mac Soundflower tool was employed to evaluate the recognition performance using a continuous speech corpus that contains 2.63 h (by 10 male and 10 female speakers) of modern standard Arabic (MSA) broadcast news. The experimental results indicate recognition accuracy at 54.02%, and the accuracies for the male and female speakers are almost the same. This result promotes the need for further research to expose the practical range of accuracy. The performance’s decline might be an indication of the necessity for further research to boost the overall recognition accuracy.