# Yet Another Model for Arabic Dialect Identification

arXiv: https://arxiv.org/abs/2310.13812 · HTML: https://mlsanity.com/paper/2310.13812/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Speech Recognition Challenge in the Wild: Arabic MGB-3](https://mlsanity.com/paper/1709.07276/) (2017)
- [UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training](https://mlsanity.com/paper/2110.05752/) (2021)
- [On the Robustness of Arabic Speech Dialect Identification](https://mlsanity.com/paper/2306.03789/) (2023)
- [MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge](https://mlsanity.com/paper/1709.00387/) (2017)
- [Towards spoken dialect identification of Irish](https://mlsanity.com/paper/2307.07436/) (2023)
- [UTD-CRSS Submission for MGB-3 Arabic Dialect Identification: Front-end and Back-end Advancements on Broadcast Speech](https://mlsanity.com/paper/1710.00113/) (2017)
- [LSTM-TDNN with convolutional front-end for Dialect Identification in the 2019 Multi-Genre Broadcast Challenge](https://mlsanity.com/paper/1912.09003/) (2019)

## Built on by (most cited first, 7 of 7)

- [NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2407.04910/) (2024)
- [ADI-20: Arabic Dialect Identification dataset and models](https://mlsanity.com/paper/2511.10070/) (2025)
- [HARNESS: Lightweight Distilled Arabic Speech Foundation Models](https://mlsanity.com/paper/2509.14689/) (2025)
- [MoRoVoc: A Large Dataset for Geographical Variation Identification of the Spoken Romanian Language](https://mlsanity.com/paper/2509.16781/) (2025)
- [Voice Conversion Improves Cross-Domain Robustness for Spoken Arabic Dialect Identification](https://mlsanity.com/paper/2505.24713/) (2025)
- [CTC-DID: CTC-Based Arabic dialect identification for streaming applications](https://mlsanity.com/paper/2601.12199/) (2026)
- [HARNESS: Lightweight Distilled Arabic Speech Foundation Models](https://mlsanity.com/paper/2604.14186/) (2026)

## Abstract (arXiv)

In this paper, we describe a spoken Arabic dialect identification (ADI) model for Arabic that consistently outperforms previously published results on two benchmark datasets: ADI-5 and ADI-17. We explore two architectural variations: ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and features exratected from the pre-trained self-supervised model UniSpeech-SAT Large, as well as a fusion of all four variants. We find that individually, ECAPA-TDNN network outperforms ResNet, and models with UniSpeech-SAT features outperform models with MFCCs by a large margin. Furthermore, a fusion of all four variants consistently outperforms individual models. Our best models outperform previously reported results on both datasets, with accuracies of 84.7% and 96.9% on ADI-5 and ADI-17, respectively.