Syllable-based rhythm and voice conversion of dysarthric speech, trained without labels, improves LF-MMI ASR word error rates on Torgo, especially for severe speakers, but not for fine-tuned Whisper.
Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
eess.AS 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
Syllable-based rhythm and voice conversion of dysarthric speech, trained without labels, improves LF-MMI ASR word error rates on Torgo, especially for severe speakers, but not for fine-tuned Whisper.