GigaSpeech 3: Scaling Multi-Dialect Arabic Speech Recognition with Weakly Labeled Data
GigaSpeech 3 is presented as a large-scale, weakly labeled Arabic speech corpus of about 10 million segments across Algerian, Egyptian, Iraqi, Saudi, and Syrian Arabic, supporting dialect-specific and multi-dialect ASR and low-resource…
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
- Linked in sourceSpeechColab/GigaSpeechBench
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GigaSpeech 3 is presented as a large-scale, weakly labeled Arabic speech corpus of about 10 million segments across Algerian, Egyptian, Iraqi, Saudi, and Syrian Arabic, supporting dialect-specific and multi-dialect ASR and low-resource research. The accompanying manuscript reports about 39,000 hours for its study corpus. Starting from only 51–63 hours of manual transcription per dialect, the authors combine ASR-biased self-supervised learning, automatic transcription, data selection, and noisy-student refinement to build audio–text supervision. Training data, an online demo, and an evaluation benchmark are linked.
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Corpus contains about 10 million speech segments across five Arabic dialects: DZA, EGY, IRQ, SAU, and SYR.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseManuscript reports about 39,000 hours for its study corpus.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseStarts from only 51–63 hours of manual transcription per dialect before automatic labeling.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphrasePipeline combines ASR-biased self-supervised learning, automatic transcription, data selection, and noisy-student refinement.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
Source claims not independently checked; the text does not report recognition accuracy results, and the training recipes and model checkpoints are described as not yet available.
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Why this may matter
Dialectal Arabic has limited labeled speech, so a reusable weakly labeled corpus with released evaluation data could support low-resource ASR research across several dialects.
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