No abstract

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 UP-DRS 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.

Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computerassisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy. 1

Current automatic deception detection approaches tend to rely on cues that are based either on specific lexical items or on linguistically abstract features that are not necessarily motivated by the psychology of deception. Notably, while approaches relying on such features can do well when the content domain is similar for training and testing, they suffer when content changes occur. We investigate new linguistically defined features that aim to capture specific details, a psychologically motivated aspect of truthful versus deceptive language that may be diagnostic across content domains. To ascertain the potential utility of these features, we evaluate them on data sets representing a broad sample of deceptive language, including hotel reviews, opinions about emotionally charged topics, and answers to job interview questions. We additionally evaluate these features as part of a deception detection classifier. We find that these linguistically defined specific detail features are most useful for cross-domain deception detection when the training data differ significantly in content from the test data, and particularly benefit classification accuracy on deceptive documents. We discuss implications of our results for general-purpose approaches to deception detection.

Different modes of vibration of the vocal folds contribute significantly to the voice quality. The neutral mode phonation, often said as a modal voice, is one against which the other modes can be contrastively described, called also 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 deep learning approach. Five different non-modal phonations are considered: falsetto, creaky, harshness, tense and breathiness are considered, and their impact 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.