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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.
A groundbreaking dataset of 313 hours of real-world code-switched speech reveals rich patterns and frequencies previously overlooked in multilingual research.
Dissimilarity, not just similarity, unlocks better language generalization for low-resource varieties.
Forget hand-tuning: this recipe for universal phone recognition leverages large-scale multilingual data and SSL to achieve SOTA performance across 100+ languages.
