Toward Building Another Arabic Voice Command Dataset for Multiple Speech Processing Tasks

Expanding Internet connectivity has had tremendous influence on people’s everyday life, since they do everything on their phones and laptops [1]. Several items have been developed by various researchers in order to improve the lives of people, notably the elderly and disabled, while remaining technologically advanced. Voice-command-enabled technologies, such as SIRI and Google voice commands, are the most useful. These systems are based on the Speech recognition module, which is one of the most important module that can make human-machine communication easier. Automatic Speech Recognition (ASR) has made significant progress toward human-like performance by employing a data-driven method [2]. In this paper, we created an Arabic voice command dataset which include 10 commands spoken by 10 speaker and repeated 10 times. The obtained dataset, despite its size, was evaluated on four speech processing tasks and achieved an accuracy of 95.9% in ASR, and a macro F1score of 99.67%, 100%, 100%, and 97.98%, in speaker identification, gender recognition, accent recognition, and spoken language understanding, respectively.