Speech deficits are common symptoms among Parkinson's Disease (PD) patients. The automatic assessment of speech signals is promising for the evaluation of the neurological state and the speech quality of the patients. Recently, progress has been made in applying machine learning and computational methods to automatically evaluate the speech of PD patients. In the present study, we plan to analyze the speech signals of PD patients and healthy control (HC) subjects in three different languages: German, Spanish, and Czech, with the aim to identify biomarkers to discriminate between PD patients and HC subjects and to evaluate the neurological state of the patients. Therefore, the main contribution of this study is the automatic classification of PD patients and HC subjects in different languages with focusing on phonation, articulation, and prosody. We will focus on an intelligibility analysis based on automatic speech recognition systems trained on these three languages. This is one of the first studies done that considers the evaluation of the speech of PD patients in different languages. The purpose of this research proposal is to build a model that can discriminate PD and HC subjects even when the language used for train and test is different.
The number of people who attend virtual meetings has increased as a result of COVID-19. In this paper, we present a system that consists of an expressive humanoid social robot called QTRobot, and a recommender system that employs natural language processing techniques to recommend images related to the content of the presenter's speech to the audience in real time. This is achieved utilising the QTRobot's platform capabilities (microphone, computation power, and Wi-Fi).
Motivational speaking usually conveys a highly emotional message and its purpose is to invite action. The goal of this paper is to investigate the prosodic realization of one particular type of cheering, namely inciting cheering for single addressees in sport events (here, long-distance running), using the name of that person.31 native speakers of German took part in the experiment. They were asked to cheer up an individual marathon runner in a sporting event represented by video by producing his or her name (1-5 syllables long). For reasons of comparison, the participants also produced the same names in isolation and carrier sentences. Our results reveal that speakers use different strategies to meet their motivational communicative goals: while some speakers produced the runners' names by dividing them into syllables, others pronounced the names as quickly as possible putting more emphasis on the first syllable. A few speakers followed a mixed strategy.Contrary to our expectations, it was not the intensity that mostly contributes to the differences between the different speaking styles (cheering vs. neutral), at least in the methods we were using. Rather, participants employed higher fundamental frequency and longer duration when cheering for marathon runners.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.