Artificial intelligence-generated Arabic subtitles: insights from Veed.io’s automatic speech recognition system of Jordanian Arabic
Wala’ Mohammad Akasheh, Ahmad S. Haider, Bassam Saideen, Yousef Sahari
Abstract
Abstract This paper examines the errors that the automatic speech recognition (ASR) system of Veed.io produces when transcribing utterances spoken in Jordanian Arabic into subtitles. It attempts to propose a new classification for the subtitles that are built based on artificial intelligence technology. Through a combination of qualitative and quantitative analyses, the study examines the types of errors and their impact on comprehension. The errors observed in the generated subtitles based on linguistic and phonetic analysis are categorised into three main types: deletions, substitutions, and insertions. Furthermore, the quantitative analysis measures the word error rate (WER) and shows that the WER percentage is 38.857% revealing that deletions are the most common type of error, followed by substitutions and insertions. The study recommends conducting further research on ASR systems for Arabic language dialects and advises subtitlers to be aware of the limitations of these systems when using them, ensuring that they edit and supervise them appropriately.