LLM generative error correction improves low-resource Frisian ASR performance, with comparable gains on a contamination-controlled offline dataset confirming true correction ability.
XLS-R: Self-supervised Cross-lingual Speech Rep- resentation Learning at Scale,
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Diffusion reconstruction creates hard samples for audio deepfake detection training, and when paired with feature aggregation and RACL, it reduces average EER versus baselines.
citing papers explorer
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Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian
LLM generative error correction improves low-resource Frisian ASR performance, with comparable gains on a contamination-controlled offline dataset confirming true correction ability.
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Diffusion Reconstruction towards Generalizable Audio Deepfake Detection
Diffusion reconstruction creates hard samples for audio deepfake detection training, and when paired with feature aggregation and RACL, it reduces average EER versus baselines.