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University of Illinois Urbana-Champaign
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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.
RTFree-F5 achieves a remarkable 10.4% WER on dysarthric speech without needing any reference transcripts, surpassing even ground-truth baselines.
Flexible alignments in speech representation learning can boost adaptability across varying speaking rates, challenging the need for strict frame-wise correspondence.
LLMs can guide phoneme editing to create synthetic accented speech from just a handful of examples, substantially improving ASR accuracy where training data is scarce.
