  Automated discourse analysis tools based on Natural Language Processing (NLP)
aiming at the diagnosis of language-impairing dementias generally extract
several textual metrics of narrative transcripts. However, the absence of
sentence boundary segmentation in the transcripts prevents the direct
application of NLP methods which rely on these marks to function properly, such
as taggers and parsers. We present the first steps taken towards automatic
neuropsychological evaluation based on narrative discourse analysis, presenting
a new automatic sentence segmentation method for impaired speech. Our model
uses recurrent convolutional neural networks with prosodic, Part of Speech
(PoS) features, and word embeddings. It was evaluated intrinsically on
impaired, spontaneous speech, as well as, normal, prepared speech, and presents
better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive
Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method
(F1 = 0.55 and 0.53, respectively) used in the same context of our study. The
results suggest that our model is robust for impaired speech and can be used in
automated discourse analysis tools to differentiate narratives produced by MCI
and CTL.
