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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
This paper proposed a novel approach for the detection and reconstruction of
dysarthric speech. The encoder-decoder model factorizes speech into a
low-dimensional latent space and encoding of the input text. We showed that the
latent space conveys interpretable characteristics of dysarthria, such as
intelligibility and fluency of speech. MUSHRA perceptual test demonstrated that
the adaptation of the latent space let the model generate speech of improved
fluency. The multi-task supervised approach for predicting both the probability
of dysarthric speech and the mel-spectrogram helps improve the detection of
dysarthria with higher accuracy. This is thanks to a low-dimensional latent
space of the auto-encoder as opposed to directly predicting dysarthria from a
highly dimensional mel-spectrogram.Comment: 5 pages, 5 figures, Accepted for Interspeech 201