Ingo Siegert

In mental health care, fear of social stigma can prevent people from seeking therapy. Video-based remote therapy can lower this barrier, but for highly stigmatized or legally sensitive conditions, additional privacy protection may be required. Our prior work used a landmark “mask” representation for emotion-preserving facial anonymization; in this extension, we introduce humanoid avatarization and directly compare both approaches. In an online user study (N = 33), participants performed open-set human re-identification and human emotion recognition on anonymized videos and reported preferences in an A/B comparison. Participants rated the avatar as more natural and expressive, while rating the mask as more anonymous and private. In contrast, objective re-identification accuracy was lower for the avatar than for the mask (13.46% vs. 20.20%), indicating stronger privacy protection by avatarization despite lower perceived anonymity. Emotion recognition was generally higher with the avatar method, consistent with participants’ utility-oriented preferences.

There has been a surge of interest in the use of speech as a biomarker for a wide range of health conditions, based on the premise that neurological, mental or physical impairments affecting speech production can be objectively assessed by automated analysis. Recent advances in clinical speech artificial intelligence (AI) have applied supervised learning techniques, similar to those used in general-purpose speech technology applications, to support the diagnosis and monitoring of mental health, cognitive decline, and motor disorders. Although clinical speech AI offers significant promise as a scalable and low-burden tool for health assessment, it also presents unique challenges compared to other speech technology applications. These include the need for condition-specific speech elicitation tasks, limited and heterogeneous datasets, sensitive data collection protocols, diverse speech representation and modeling strategies, and uncertainty in ground-truth clinical labels. This overview synthesizes the emerging literature that addresses these challenges in the full clinical speech AI pipeline: from speech elicitation and recording to representation, model development, and deployment. We provide a practical review of the field, including the technical pipeline for clinical AI, the design of clinical speech tasks, data collection practices, traditional and clinically oriented speech representations, predictive modeling approaches, and ethical considerations. We also discuss open technical and translational challenges and outline future research directions. The goal is to equip researchers and algorithm developers with the knowledge needed to move clinical speech AI from experimental development to real-world clinical impact.

This study investigates students’ readiness to adopt Generative AI (GenAI) in their learning and their ethical considerations across four countries: the United Kingdom, Germany, Poland and Israel. Data were collected from February to May 2024 from 869 students through a questionnaire addressing GenAI readiness and ethical considerations. The findings reveal marked differences in students’ readiness for GenAI, with Israel and Poland generally leading in most aspects of GenAI readiness, particularly in understanding and practical knowledge, while the UK and Germany often show lower readiness levels, though this varies in specific aspects. A notable finding is the low access to paid and institutional tools across all countries. Ethical perspectives on GenAI usage varied significantly among the four countries, reflecting diverse cultural attitudes toward technology, with Polish and Israeli students generally showing higher awareness of ethical concerns, while UK students appear less concerned about potential ethical issues. German students tend to take a moderate position but show particular concern about unauthorized use. The findings highlight the importance of developing culturally sensitive approaches to GenAI integration in higher education that consider local institutional frameworks and educational traditions. This research contributes to understanding the complex interplay between cultural factors and technology adoption in education, suggesting that successful GenAI implementation requires tailored strategies that account for diverse cultural contexts while addressing universal challenges of accessibility and ethical use.

This paper presents the design and the explanatory approach of a digital music intervention, termed "music time-out," aimed at enhancing psychosomatic post-treatment for patients with mental disorders and physical comorbidities after a hospital stay. By utilizing a digital voice assistant (DVA), the intervention facilitates attentive music listening, fostering self-awareness and emotional regulation. Grounded in theory and research from music psychology and music therapy the DVA guides patients through relaxation, imaginative exploration, and emotional reflection. Potential benefits in terms of self-care are weighed against the technology-related challenges and data handling. Further clinical application and evaluation are proposed to assess therapeutic effectiveness and user experience.