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

TL;DR

This paper presents hybrid deep learning and signal processing models for Arabic dialect recognition in low-resource settings, demonstrating that spectral features combined with CNNs significantly improve accuracy over wavelet-based RNNs.

Contribution

It introduces and evaluates two hybrid models combining classical signal processing with deep learning for Arabic dialect recognition in low-resource scenarios, highlighting the effectiveness of spectral features with CNNs.

Findings

MFCC + CNN achieved 91.2% accuracy

Wavelet + RNN achieved 66.5% accuracy

Spectral features with CNN outperform wavelet-based models

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.