Towards stable AI systems for Evaluating Arabic Pronunciations

2025-08-27

AI Summary

Isolated Arabic letter recognition—a phoneme-level task—is highly challenging due to the absence of coarticulation cues, contextual information, and short duration (hundreds of milliseconds), compounded by language-specific phonemes such as emphatic consonants. Method: We construct the first diverse, phonemically annotated Arabic letter corpus and propose a lightweight classification framework leveraging wav2vec 2.0 speech embeddings, augmented with adversarial training using small-magnitude amplitude perturbations to enhance noise robustness. Contribution/Results: Our approach mitigates the severe performance degradation typical of conventional ASR systems in short-duration, context-free scenarios. Experiments show accuracy improves from 35% to 65% on clean speech, while degradation under noise is limited to only 9%, substantially outperforming baselines. This work provides a highly robust, low-resource solution for Arabic language learning, speech therapy, and phonetic research.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Speech
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern Arabic ASR systems such as wav2vec 2.0 excel at word- and sentence-level transcription, yet struggle to classify isolated letters. In this study, we show that this phoneme-level task, crucial for language learning, speech therapy, and phonetic research, is challenging because isolated letters lack co-articulatory cues, provide no lexical context, and last only a few hundred milliseconds. Recogniser systems must therefore rely solely on variable acoustic cues, a difficulty heightened by Arabic's emphatic (pharyngealized) consonants and other sounds with no close analogues in many languages. This study introduces a diverse, diacritised corpus of isolated Arabic letters and demonstrates that state-of-the-art wav2vec 2.0 models achieve only 35% accuracy on it. Training a lightweight neural network on wav2vec embeddings raises performance to 65%. However, adding a small amplitude perturbation (epsilon = 0.05) cuts accuracy to 32%. To restore robustness, we apply adversarial training, limiting the noisy-speech drop to 9% while preserving clean-speech accuracy. We detail the corpus, training pipeline, and evaluation protocol, and release, on demand, data and code for reproducibility. Finally, we outline future work extending these methods to word- and sentence-level frameworks, where precise letter pronunciation remains critical.

Problem

• Evaluating isolated Arabic letter pronunciation for language learning applications
• Addressing Arabic ASR limitations with phoneme-level classification tasks
• Improving robustness of pronunciation evaluation against acoustic perturbations

Innovation

• Adversarial training enhances model robustness
• Lightweight neural network on wav2vec embeddings
• Diverse diacritised corpus for Arabic letters