Hybrid Deep Learning And Signal Processing For Arabic Dialect Recognition In Low-resource Settings

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 Wavel

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyalshwayyat2025hybrid

Related papers

• A Deep Learning Approach For Similar Languages, Varieties And Dialects (2019)0.00
• Dialectal Coverage And Generalization In Arabic Speech Recognition (2024)4.52
• Efficient Arabic Emotion Recognition Using Deep Neural Networks (2020)11.93
• Convolutional Neural Networks And Language Embeddings For End-to-end Dialect Recognition (2018)12.40
• MIT-QCRI Arabic Dialect Identification System For The 2017 Multi-genre Broadcast Challenge (2017)8.60
• A Highly Adaptive Acoustic Model For Accurate Multi-dialect Speech Recognition (2022)10.85
• UTD-CRSS Submission For MGB-3 Arabic Dialect Identification: Front-end And Back-end Advancements On Broadcast Speech (2017)4.52
• Whisper Turns Stronger: Augmenting Wav2vec 2.0 For Superior ASR In Low-resource Languages (2024)0.00