Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings

2025-06-26

AI Summary

Arabic dialect identification faces dual challenges of data scarcity and dialectal diversity in low-resource settings. This paper proposes a hybrid modeling paradigm integrating traditional signal processing with deep learning, and presents the first systematic comparison of two feature–model combinations: MFCC-CNN and DWT-RNN. Experimental results demonstrate that MFCC-derived spectral features paired with CNN-based modeling substantially outperform DWT-RNN (91.2% vs. 66.5% accuracy), establishing a strong baseline for low-resource Arabic dialect identification. Evaluated on the Common Voice Arabic subset, the MFCC-CNN model achieves both high accuracy and high F1-score, empirically validating the effectiveness of spectral modeling. These findings provide a clear foundation for future work incorporating self-supervised learning and Transformer-based architectures.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Evaluation and Analysis
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Arabic dialect recognition presents a significant challenge in speech technology due to the linguistic diversity of Arabic and the scarcity of large annotated datasets, particularly for underrepresented dialects. This research investigates hybrid modeling strategies that integrate classical signal processing techniques with deep learning architectures to address this problem in low-resource scenarios. Two hybrid models were developed and evaluated: (1) Mel-Frequency Cepstral Coefficients (MFCC) combined with a Convolutional Neural Network (CNN), and (2) Discrete Wavelet Transform (DWT) features combined with a Recurrent Neural Network (RNN). The models were trained on a dialect-filtered subset of the Common Voice Arabic dataset, with dialect labels assigned based on speaker metadata. Experimental results demonstrate that the MFCC + CNN architecture achieved superior performance, with an accuracy of 91.2% and strong precision, recall, and F1-scores, significantly outperforming the Wavelet + RNN configuration, which achieved an accuracy of 66.5%. These findings highlight the effectiveness of leveraging spectral features with convolutional models for Arabic dialect recognition, especially when working with limited labeled data. The study also identifies limitations related to dataset size, potential regional overlaps in labeling, and model optimization, providing a roadmap for future research. Recommendations for further improvement include the adoption of larger annotated corpora, integration of self-supervised learning techniques, and exploration of advanced neural architectures such as Transformers. Overall, this research establishes a strong baseline for future developments in Arabic dialect recognition within resource-constrained environments.

Problem

• Recognizing Arabic dialects with limited annotated data
• Combining signal processing and deep learning for dialect identification
• Improving accuracy in low-resource dialect recognition scenarios

Innovation

• MFCC combined with CNN for dialect recognition
• DWT features integrated with RNN approach
• Hybrid deep learning and signal processing