A U-Net trained on synthetically warped healthy speech predicts local Lie-group distortion fields and applies the approximate inverse, improving zero-shot ASR on dysarthric speech.
The Success of AdaBoost and Its Application in Portfolio Management
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
We develop a novel approach to explain why AdaBoost is a successful classifier. By introducing a measure of the influence of the noise points (ION) in the training data for the binary classification problem, we prove that there is a strong connection between the ION and the test error. We further identify that the ION of AdaBoost decreases as the iteration number or the complexity of the base learners increases. We confirm that it is impossible to obtain a consistent classifier without deep trees as the base learners of AdaBoost in some complicated situations. We apply AdaBoost in portfolio management via empirical studies in the Chinese market, which corroborates our theoretical propositions.
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Dysarthria Normalization via Local Lie Group Transformations for Robust ASR
A U-Net trained on synthetically warped healthy speech predicts local Lie-group distortion fields and applies the approximate inverse, improving zero-shot ASR on dysarthric speech.