TajweedAI: A Hybrid ASR-Classifier for Real-Time Qalqalah Detection in Quranic Recitation

Nabhan Mazid, Muaz Ahmad

Keywords: Tajweed, Quranic Recitation, Pronunciation Assessment, CAPT, Speech Recognition, Qalqalah, Hard Negative Mining, Muslims in ML, Arabic NLP

TL;DR: We use a "hard negative mining" training strategy for a classifier in a four-step architecture (transcribe, segment, classify, feedback) to provide real-time analysis of the Quranic pronunciation rule of Qalqalah.

Abstract: Proper recitation of the Holy Quran is governed by a complex set of phonetic rules known as Tajweed, where minor pronunciation errors can significantly alter meaning. While modern Artificial Intelligence (AI) tools excel at transcription, they largely lack the capability to provide corrective feedback on pronunciation quality. This paper introduces TajweedAI, a novel system designed to bridge this gap by offering real-time, fine-grained phonetic analysis for Quranic learners. We present a hybrid architecture that combines a state-of-the-art Automatic Speech Recognition (ASR) model for temporal alignment with a dedicated binary classifier for phonetic rule verification. As a case study, we focus on the acoustically complex Tajweed rule of Qalqalah—the characteristic "echoing" of specific plosive consonants. This paper details an iterative experimental methodology, beginning with a baseline model achieving 58.33% accuracy and culminating in a highly specialized classifier trained via hard negative mining. This final model achieved 100% accuracy on its specialized internal validation set for the challenging case of the word al-Falaq. However, a limited external evaluation indicated challenges in generalization, yielding 57.14% accuracy. This work validates a scalable framework for automated Tajweed correction, presenting a significant step for Computer-Assisted Pronunciation Training (CAPT) in Quranic studies.

Submission Number: 73