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Experiments conducted on the UASpeech and TORGO corpora suggest that Conformer models trained using the DSI tokens outperform the comparable baseline HuBERT discrete/continuous features by statistically significant WER reductions.
Zero-shot adaptation can reduce word error rates by over 0.6% while achieving nearly 10 times faster real-time processing for elderly speech recognition.
Speaker adaptive training combined with confidence-guided pseudo-labeling leads to substantial gains in elderly speech recognition accuracy, outperforming conventional methods.
Personalized federated learning can reduce word error rates in dysarthric speech recognition by up to 0.99%, highlighting the potential for tailored ASR solutions.
