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This paper proposed a novel approach for the detection and reconstruction ofdysarthric speech. The encoder-decoder model factorizes speech into alow-dimensional latent space and encoding of the input text. We showed that thelatent space conveys interpretable characteristics of dysarthria, such asintelligibility and fluency of speech. MUSHRA perceptual test demonstrated thatthe adaptation of the latent space let the model generate speech of improvedfluency. The multi-task supervised approach for predicting both the probabilityof dysarthric speech and the mel-spectrogram helps improve the detection ofdysarthria with higher accuracy. This is thanks to a low-dimensional latentspace of the auto-encoder as opposed to directly predicting dysarthria from ahighly dimensional mel-spectrogram.

Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech