UrduSpeech is a 156-hour high-fidelity Urdu speech corpus with 12-dimension paralinguistic annotations, a 9-hour manually corrected benchmark, and open-source release to support speech technology for an under-resourced language.
Fleurs: Few-shot learning evaluation of universal representations of speech
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
The IQRA 2026 challenge on Arabic mispronunciation detection reports a 0.28 F1-score gain from new authentic human error data and diverse modeling approaches including self-supervised and audio-language models.
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UrduSpeech: A 156-Hour Urdu Speech Corpus with 12-Dimension Paralinguistic Annotations
UrduSpeech is a 156-hour high-fidelity Urdu speech corpus with 12-dimension paralinguistic annotations, a 9-hour manually corrected benchmark, and open-source release to support speech technology for an under-resourced language.
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IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA)
The IQRA 2026 challenge on Arabic mispronunciation detection reports a 0.28 F1-score gain from new authentic human error data and diverse modeling approaches including self-supervised and audio-language models.