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Simulating dysarthric speech for training data augmentation in clinical speech applications
[article]
2018
arXiv
pre-print
Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is prohibitively expensive because of privacy concerns or lack of access. As a result, clinical speech applications are typically developed using small data sets with only tens of speakers. In this paper, we propose a method for simulating training data for clinical applications by transforming healthy speech to
arXiv:1804.10325v1
fatcat:txbs6yfjpfegpddbl6qo7udiym