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The results imply that existing deepfake speech detection methods are insufficient for reliably detecting edit-based speech deepfakes in various scenarios unseen during training.
Achieving a 40% reduction in character error rate, this syllable-level UASR framework unlocks new potential for low-resource language recognition without costly phoneme resources.
Phonological features can be extracted from self-supervised speech models in under a minute, achieving state-of-the-art performance in phone segmentation and recognition.
