  Objective speech disorder classification for speakers with communication
difficulty is desirable for diagnosis and administering therapy. With the
current state of speech technology, it is evident to propose neural networks
for this application. But neural network model training is hampered by a lack
of labeled disordered speech data. In this research, we apply an extended
version of Factorized Hierarchical Variational Auto-encoders (FHVAE) for
representation learning on disordered speech. The FHVAE model extracts both
content-related and sequence-related latent variables from speech data, and we
utilize the extracted variables to explore how disorder type information is
represented in the latent variables. For better classification performance, the
latent variables are aggregated at the word and sentence level. We show that an
extension of the FHVAE model succeeds in the better disentanglement of the
content-related and sequence-related related representations, but both
representations are still required for best results on disorder type
classification.
