GigaSpeech 3: Scaling Multi-Dialect Arabic Speech Recognition with Weakly Labeled Data
Abstract
Dialectal Arabic speech recognition remains constrained by the scarcity of dialect-resolved transcribed speech despite the abundance of web audio. We introduce GigaSpeech 3, a corpus of 39,000 hours and 10 million weakly transcribed speech segments covering Algerian, Egyptian, Iraqi, Saudi, and Syrian Arabic. Starting from 60 hours of manual transcription per dialect, we train labeling models using speech-recognition-biased self-supervised learning and apply them to dialect-matched web audio. Data selection and four rounds of noisy-student refinement for Saudi and Syrian Arabic yield the final corpus. We validate its utility through dialect-specific and multi-dialect speech recognition training and cross-domain evaluation. On GigaSpeechBench, the final dialect-specific models outperform their labeling models across all five dialects and achieve a macro-average word error rate of 23.86%, a 16.6% relative reduction from the strongest third-party system covering all five dialects, while outperforming the best third-party result on three dialects. A multi-dialect model using self-predicted dialect identities achieves a macro-average word error rate of 25.51% while also supporting Modern Standard Arabic. Across eight conditions from three external corpora, a GigaSpeech 3-trained Zipformer achieves the lowest word error rate among six evaluated models in every condition, while adapting the Qwen3 speech recognizer on GigaSpeech 3 improves performance across all eight conditions. We release the corpus and recipes as a foundation for dialect-aware Arabic speech processing.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents GigaSpeech 3, a large-scale weakly labeled corpus for five Arabic dialects, constructed via ASR-biased self-supervised learning and noisy-student refinement, demonstrating that such weak supervision enables compact models to outperform strong third-party systems and large pretrained models in dialect-specific and multi-dialect recognition tasks.
Methodology
The paper proposes a data-centric approach to low-resource dialectal Arabic ASR. The core methodology involves constructing a large-scale weakly labeled corpus (GigaSpeech 3) using a "VietASR" pipeline. This pipeline leverages ASR-biased self-supervised learning (SSL) to train labeling models from small manual seeds (60 hours/dialect), which are then applied to dialect-matched web audio. The process includes data selection and iterative Noisy Student Training (NST) refinement for specific dialects (Saudi, Syrian). The novelty lies not in a new model architecture, but in the rigorous application of existing SSL and weak-labeling techniques to a specific, under-served linguistic domain (Arabic dialects) at a significant scale (39k hours). The validation strategy is sound: training independent downstream models (Zipformer) from scratch on the weak labels to prove the utility of the data beyond the labeling model's parameters.
Results & evaluation
The experimental evaluation is comprehensive and rigorous. The authors validate the corpus by showing that models trained on the weak labels outperform the labeling models themselves, confirming the data's utility. They compare against strong third-party baselines (including commercial APIs and large open-source models like Qwen3 and Whisper) on GigaSpeechBench, achieving state-of-the-art results for compact models. Cross-domain evaluation on MASC, Casablanca, and SADA further demonstrates generalizability. The inclusion of a multi-dialect model with automatic language identification (LID) and the analysis of routing errors adds depth to the evaluation. The results are consistent and well-supported by ablation studies (e.g., Labeled-only vs. SSL+FT vs. Weak-labels).
Reproducibility
High. The authors release the code, dataset, and demo. The pipeline is described in detail, including the specific stages of the VietASR recipe and the NST refinement process. The use of standard architectures (Zipformer) and public benchmarks (GigaSpeechBench) enhances reproducibility. The separation of training and test channels at the source level is a strong methodological choice that is clearly documented.
Limitations
The primary limitation is the reliance on web audio, which may contain noise, code-switching, or non-dialectal content that is difficult to fully filter. The country-level dialect labels are coarse and may not capture intra-country variation. The multi-dialect model's performance is affected by LID errors, particularly for Egyptian and Syrian dialects, as shown in the confusion matrix. The paper focuses on five specific dialects, leaving other Arabic varieties unaddressed.
Broader impact
This work has significant impact on the field of low-resource speech recognition. By providing a large, dialect-resolved corpus and a validated pipeline for weak labeling, it enables the development of more accurate and inclusive ASR systems for Arabic speakers. The methodology can be adapted to other low-resource languages or dialects. The release of the corpus and tools will likely accelerate research in dialectal Arabic processing and serve as a benchmark for future work. The paper presents GigaSpeech 3, a large-scale weakly labeled corpus for five Arabic dialects, constructed via ASR-biased self-supervised learning and noisy-student refinement, demonstrating that such weak supervision enables compact models to outperform strong third-party systems and large pretrained models in dialect-specific and multi-dialect recognition tasks.
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