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A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment
We are the first to evaluate the representations from pre-trained state-of-the-art Self-Supervised models across three downstream tasks on dysarthric speech: disease classification, word recognition and intelligibility classification, and under three noise scenarios on the UA-Speech dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removal
Despite impressive empirical advances of SSL in solving various tasks, the problem of understanding and characterizing SSL representations learned from input data remains relatively under-explored.
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data
Nevertheless, there is limited research investigating the impact of imbalance in the contribution of different features in an error vector.
