  This paper considers a representation learning strategy to model speech
signals from patients with Parkinson's disease and cleft lip and palate. In
particular, it compares different parametrized representation types such as
wideband and narrowband spectrograms, and wavelet-based scalograms, with the
goal of quantifying the representation capacity of each. Methods for
quantification include the ability of the proposed model to classify different
pathologies and the associated disease severity. Additionally, this paper
proposes a novel fusion strategy called multi-spectral fusion that combines
wideband and narrowband spectral resolutions using a representation learning
strategy based on autoencoders. The proposed models are able to classify the
speech from Parkinson's disease patients with accuracy up to 95\%. The proposed
models were also able to asses the dysarthria severity of Parkinson's disease
patients with a Spearman correlation up to 0.75. These results outperform those
observed in literature where the same problem was addressed with the same
corpus.
