Automatic Speech Recognition (ASR) offers a scalable and cost-efficient alternative to manual transcription and is becoming increasingly relevant in clinical contexts, particularly for the detection of cognitive decline and mental health assessment. However, current ASR-systems still struggle with spontaneous speech, particularly when processing disfluencies, pauses, and speaker variability that often carry diagnostic value. This study evaluates state-of-the-art open ASR models targeting Swedish using recordings from the "Trip to Stockholm" discourse narrative task which elicits ecologically valid, cognitively demanding speech. Recognition quality is assessed using various metrics, alongside an analysis of linguistic and technical sources of error focused on disfluencies. Our findings show that disfluency-related phenomena degrade recognition performance. Possible post-processing strategies can improve specific error patterns emerging for filled pauses, word repetitions, and self-corrections. The results illustrate both the advances and ongoing limitations of ASR for spontaneous Swedish speech, emphasizing the need for models explicitly trained, or fine-tuned, on disfluent data to ensure robustness in clinical and research applications.

The QUEST (QUality ESTablished) project aims at ensuring the reusability of audio-visual datasets (Wamprechtshammer et al., 2022) by devising quality criteria and curating processes. RefCo (Reference Corpora) is an initiative within QUEST in collaboration with DoReCo (Documentation Reference Corpus, Paschen et al. (2020)) focusing on language documentation projects. Previously, Aznar and Seifart (2020) introduced a set of quality criteria dedicated to documenting fieldwork corpora. Based on these criteria, we establish a semi-automatic review process for existing and work-in-progress corpora, in particular for language documentation. The goal is to improve the quality of a corpus by increasing its reusability. A central part of this process is a template for machine-readable corpus documentation and automatic data verification based on this documentation. In addition to the documentation and automatic verification, the process involves a human review and potentially results in a RefCo certification of the corpus. For each of these steps, we provide guidelines and manuals. We describe the evaluation process in detail, highlight the current limits for automatic evaluation and how the manual review is organized accordingly.

MULLE is a tool for language learning that focuses on teaching Latin as a foreign language. It is aimed for easy integration into the traditional classroom setting and syllabus, which makes it distinct from other language learning tools that provide standalone learning experience. It uses grammar-based lessons and embraces methods of gamification to improve the learner motivation. The main type of exercise provided by our application is to practice translation, but it is also possible to shift the focus to vocabulary or morphology training.