Utilization of Artificial Intelligence with Text-To-Speech Technology Based on Natural Language Processing to Enhance Arabic Listening Skills for Non-Native Speakers - Dedi Mulyanto; Muhammad Wahyudi; Arsyad Muhammad Ali Ridho; Muhammad Zaki (Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan, 2024)
This research addresses the challenge faced by non-native speakers in mastering Arabic listening skills. The primary purpose of this study is to explore the effectiveness of Artificial Intelligence (AI)-driven text-to-speech (TTS) technology, leveraging Natural Language Processing (NLP), in improving these skills. The research employs a descriptive qualitative methodology, integrating listening tests into the design to measure learners' comprehension. These tests are administered both before and after the introduction of TTS technology, allowing for a comparative analysis of its impact. This integration ensures that the tests align with the study's objective of assessing improvements in listening proficiency among non-native Arabic learners. The results demonstrated a significant improvement in the listening skills of the participants, with notable enhancements in pronunciation, intonation, and overall comprehension. The integration of TTS technology provided learners with a consistent and accurate model of spoken Arabic, facilitating better auditory learning. The impact of this research is substantial, highlighting the potential of AI and NLP in language education, particularly for less commonly taught languages like Arabic. The study concludes that AI-based TTS technology is an effective tool for improving Arabic listening skills among non-native speakers, offering a promising avenue for future educational strategies and technological advancements in language learning.