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

• DOI: 10.21437/interspeech.2019-1206
• Publication Date: 2019-09-13T20:32:51Z
• Authors: Daniel Korzekwa; Roberto Barra-Chi...; Bozena Kostek; Thomas Drugman; Mateusz Lajszczak
• Keywords: FOS: Computer and information sciences; Sound (cs.SD); Computer Science - Computation and Language; 02 engineering and technology; Computer Science - Sound; 03 medical and health sciences; Audio and Speech Processing (eess.AS); 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; 0305 other medical science; Computation and Language (cs.CL); Electrical Engineering and Systems Science - Audio and Speech Processing

5 pages, 5 figures, Accepted for Interspeech 2019<br/>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.<br/>