Different modes of vibration of the vocal folds contribute significantly to the voice quality. The neutral mode phonation, often used in a modal voice, is one against which the other modes can be contrastively described, also called non-modal phonations. This paper investigates the impact of non-modal phonation on phonological posteriors, the probabilities of phonological features inferred from the speech signal using a deep learning approach. Five different non-modal phonations are considered: falsetto, creaky, harshness, tense and breathiness. The impact of such non-modal phonation on phonological features, the Sound Patterns of English (SPE), is investigated in both speech analysis and synthesis tasks. We found that breathy and tense phonation impact the SPE features less, creaky phonation impacts the features moderately, and harsh and falsetto phonation impact the phonological features the most. We also report invariant and the most different SPE features impacted by non-modal phonation.

Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech-based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UPDRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores.

Semantic deficit is a symptom of language impairment in Alzheimer's disease (AD).We present a generalizable method for automatic generation of information content units (ICUs) for a picture used in a standard clinical task, achieving high recall, 96.8%, of human-supplied ICUs.We use the automatically generated topic model to extract semantic features, and train a random forest classifier to achieve an F-score of 0.74 in binary classification of controls versus people with AD using a set of only 12 features.This is comparable to results (0.72 F-score) with a set of 85 manual features.Adding semantic information to a set of standard lexicosyntactic and acoustic features improves F-score to 0.80.While control and dementia subjects discuss the same topics in the same contexts, controls are more informative per second of speech.

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