Petra Jaecks

Diagnosing residual aphasia using spontaneous speech analysis

Background: It is well known that about 20% of patients with anomic aphasia still show residual aphasic symptoms 1 year post onset. As we do not have any tests to reliably diagnose residual aphasia it is difficult to decide whether these persisting problems are signs of residual aphasia that have to be treated, or are indications of a normal variability in language use, which also may occur in elderly people. Aims: The main aim of this research is to find out whether there are variables in spontaneous communication of persons with residual aphasia (P-RA) that enable us to distinguish them from persons without aphasia (P-NA). Methods & Procedures: Following a detailed linguistic analysis of spontaneous speech we employed binary logistic regression analysis to find the most relevant variables for differentiation between P-RA (N = 41) and P-NA (N = 25). Outcomes & Results: Significant group differences at different processing levels could be observed. Lexically varied, informative, and cohesively tied phrases in particular pose the main problem for P-RA. Regression analysis including variables of spontaneous communication explains 82.5% (R2) of the variation and correctly identifies 39 of 41 (95.1%) cases of P-RA and 21 of 25 (84.0%) cases of P-NA. Conclusions: Analysing spontaneous communication seems to be a promising possibility for diagnosing residual aphasia. The importance of spontaneous communication in everyday interaction, and its sensitivity to even slight deficits in language processing, promote the described analysis as a very useful screening instrument to distinguish P-RA from P-NA.

Different gaze behavior in human-robot interaction in Asperger's syndrome: An eye-tracking study

Social robots are often applied in recreational contexts to improve the experience of using technical systems, but they are also increasingly used for therapeutic purposes. In this study, we compared how patients with Autism Spectrum Disorder (ASD) interact with a social robot and a human actor. We examined the gaze behavior of nine ASD patients and 15 matched controls using a mobile eye-tracker. Participants performed a task in which they were required to follow the gaze of a robot or human actor. Our results show that ASD patients preferentially maintain eye contact during interaction with the social robot as compared to the human actor.

Disposition Recognition from Spontaneous Speech Towards a Combination with Co-speech Gestures

Speech as well as co-speech gestures are an integral part of human communicative behaviour. Furthermore, the way how these modalities influence each other and finally, reflect a speaker’s dispositional state is an important aspect of research in Human-Machine-Interaction. So far, just little is known, however, about the simultaneous investigation of both modalities. The EmoGest corpus is a novel data set addressing how emotions or dispositions manifest themselves in co-speech gestures. Participants were primed to be happy, neutral, or sad and afterwards, explain tangram figures to an experimenter. We employed this corpus to conduct disposition recognition from speech data as an evaluation of emotion priming. For the analysis, we based the classification on meaningful features already successfully applied in emotion recognition. In disposition recognition from speech, we achieved remarkable classification accuracy. These results provide the basis for a detailed disposition-related analyses of gestural behaviour, also in combination with speech. In general, the necessity of multimodal investigations of disposition is indicated which then will be heading towards an improvement of overall performance.

Web-based vs. controled environment: About the reliability of stimuli ratings in human-robot interaction

In several research areas, e.g. in the field of human-robot interaction, ratings or questionnaires are applied using offline and online methods. An argument for the use of online methods is the efficiency. By using the Internet, data can be collected much faster than in an offline experiment and the administration effort is very low. The goal of our study was to find out, if there is a difference in accuracy between an online and an offline rating task of human and robot emotional facial expressions. Results indicate, that emotional expressions are best recognized in humans (versus robots) and in the offline (versus online) condition. Furthermore, the influence of the emotional category on the accuracy rate varies between conditions. Therefore, we discuss environmental factors of online experiments that are difficult to control as main reasons for these results. We conclude that online rating studies should always be combined with more reliable offline evaluations.