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Found 5 papers, 0 papers with code
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech
Project Euphonia, a Google initiative, is dedicated to improving automatic speech recognition (ASR) of disordered speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Speech Intelligibility Classifiers from 550k Disordered Speech Samples
We developed dysarthric speech intelligibility classifiers on 551, 176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking disorders and rated for their overall intelligibility on a five-point scale.
Assessing ASR Model Quality on Disordered Speech using BERTScore
BERTScore was found to be more correlated with human assessment of error type and assessment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets
Word error rate (WER) thresholds were selected to determine Success Percentage (the percentage of personalized models reaching the target WER) in different application scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment.
