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Objective speech disorder classification for speakers with communicationdifficulty is desirable for diagnosis and administering therapy. With thecurrent state of speech technology, it is evident to propose neural networksfor this application. But neural network model training is hampered by a lackof labeled disordered speech data. In this research, we apply an extendedversion of Factorized Hierarchical Variational Auto-encoders (FHVAE) forrepresentation learning on disordered speech. The FHVAE model extracts bothcontent-related and sequence-related latent variables from speech data, and weutilize the extracted variables to explore how disorder type information isrepresented in the latent variables. For better classification performance, thelatent variables are aggregated at the word and sentence level. We show that anextension of the FHVAE model succeeds in the better disentanglement of thecontent-related and sequence-related related representations, but bothrepresentations are still required for best results on disorder typeclassification.

Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-encoders