  Conventional automatic assessment of pathological speech usually follows two
main steps: (1) extraction of pathology-specific features; (2) classification
or regression on extracted features. Given the great variety of speech and
language disorders, feature design is never a straightforward task, and yet it
is most crucial to the performance of assessment. This paper presents an
end-to-end approach to automatic speech assessment for Cantonese-speaking
People With Aphasia (PWA). The assessment is formulated as a binary
classification task to discriminate PWA with high scores of subjective
assessment from those with low scores. The sequence-to-one Recurrent Neural
Network with Gated Recurrent Unit (GRU-RNN) and Convolutional Neural Network
(CNN) models are applied to realize the end-to-end mapping from fundamental
speech features to the classification result. The pathology-specific features
used for assessment can be learned implicitly by the neural network model.
Class Activation Mapping (CAM) method is utilized to visualize how those
features contribute to the assessment result. Our experimental results show
that the end-to-end approach outperforms the conventional two-step approach in
the classification task, and confirm that the CNN model is able to learn
impairment-related features that are similar to human-designed features. The
experimental results also suggest that CNN model performs better than
sequence-to-one GRU-RNN model in this specific task.
