TY - JOUR A1 - Axenie, Cristian A1 - Halilov, Ertan A1 - Main, Julian A1 - Weiss, David T1 - Edge neuro-statistical learning for event-based visual motion detection and tracking in roadside safety systems JF - Neuromorphic Computing and Engineering N2 - The Vision Zero Program's purpose is to reduce traffic-related fatalities and serious injuries while promoting equitable, safe, and healthy mobility for all. Ultimately, the challenge is to detect pedestrians during the day and especially at night to implement safety measures. The current study introduces an award-winning low-power solution employing neuromorphic visual sensing and hybrid neuro-statistical processing developed by the Technische Hochschule Nürnberg team for the TinyML Vision Zero San Jose Competition. The solution proposes a novel neuromorphic edge fusion of spiking neural networks and event-based expectation maximization for the detection and tracking of pedestrians and bicyclists. We provide a deployment-ready evaluation of the detection performance along with robustness, energy footprint, and weatherization while emphasizing the advantages of the neuro-statistical edge solution and its city-level scaling capabilities. Y1 - 2025 U6 - https://doi.org/10.1088/2634-4386/adcbcb SN - 2634-4386 PB - IOP Publishing ER - TY - JOUR A1 - Axenie, Cristian T1 - Antifragile control systems in neuronal processing: a sensorimotor perspective JF - Biological Cybernetics N2 - The stability–robustness–resilience–adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using “canonical” neuronal computational circuits, such as Homeostatic Activity Regulation, Winner-Take-All, and Hebbian Temporal Correlation Learning, one can extend the behavior spectrum towards antifragility. Cast already in both probability theory and dynamical systems, antifragility can explain and define the interesting interplay among neural circuits, found, for instance, in sensorimotor control in the face of uncertainty and volatility. This perspective proposes a new framework to analyze and describe closed-loop neuronal processing using principles of antifragility, targeting sensorimotor control. Our objective is two-fold. First, we introduce antifragile control as a conceptual framework to quantify closed-loop neuronal network behaviors that gain from uncertainty and volatility. Second, we introduce neuronal network design principles, opening the path to neuromorphic implementations and transfer to technical systems. Y1 - 2025 U6 - https://doi.org/10.1007/s00422-025-01003-7 SN - 1432-0770 VL - 119 IS - 2-3 PB - Springer Nature ER - TY - BOOK A1 - Axenie, Cristian A1 - Bauer, Roman A1 - López Corona, Oliver A1 - West, Jeffrey T1 - Applied Antifragility in Natural Systems BT - From Principles to Applications N2 - As coined in the book of Nassim Taleb, antifragility is a property of a system to gain from uncertainty, randomness, and volatility, opposite to what fragility would incur. An antifragile system’s response to external perturbations is beyond robust, such that small stressors can strengthen the future response of the system by adding a strong anticipation component. Such principles are already well suited for describing behaviors in natural systems but also in approaching therapy designs and eco-system modelling and eco-system analysis. The purpose of this book is to build a foundational knowledge base by applying antifragile system design, analysis, and development in natural systems, including biomedicine, neuroscience, and ecology as main fields. We are interested in formalizing principles and an apparatus that turns the basic concept of antifragility into a tool for designing and building closed-loop systems that behave beyond robust in the face of uncertainty when characterizing and intervening in biomedical and ecological (eco)systems. The book introduces the framework of applied antifragility and possible paths to build systems that gain from uncertainty. We draw from the body of literature on natural systems (e.g. cancer therapy, antibiotics, neuroscience, and agricultural pest management) in an attempt to unify the scales of antifragility in one framework. The work of the Applied Antifragility Group in oncology, neuroscience, and ecology led by the authors provides a good overview on the current research status. T2 - with a foreword from Nassim Taleb Y1 - 2025 SN - 9783031903908 U6 - https://doi.org/10.1007/978-3-031-90391-5 SN - 2191-5768 PB - Springer Nature Switzerland CY - Cham ER - TY - BOOK A1 - Axenie, Cristian A1 - Akbarzadeh, Meisam A1 - Makridis, Michail A. A1 - Saveriano, Matteo A1 - Stancu, Alexandru T1 - Applied Antifragility in Technical Systems BT - From Principles to Applications N2 - The book purpose is to build a foundational knowledge base by applying antifragile system design, analysis, and development in technical systems, with a focus on traffic engineering, robotics, and control engineering. The authors are interested in formalizing principles and an apparatus that turns the basic concept of antifragility into a tool for designing and building closed-loop technical systems that behave beyond robust in the face of uncertainty. As coined in the book of Nassim Taleb, antifragility is a property of a system to gain from uncertainty, randomness, and volatility, opposite to what fragility would incur. An antifragile system’s response to external perturbations is beyond robust, such that small stressors can strengthen the future response of the system by adding a strong anticipation component. The work of the Applied Antifragility Group in traffic control and robotics, led by the authors, provides a good overview on the current research status. T2 - with a foreword from Nassim Taleb Y1 - 2025 SN - 9783031904240 U6 - https://doi.org/10.1007/978-3-031-90425-7 SN - 2191-5768 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Wagner, Dominik A1 - Churchill, Alexander A1 - Sigtia, Siddharth A1 - Marchi, Erik T1 - SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions T2 - ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - In this work, we present and evaluate SELMA, a Speech-Enabled Language Model for virtual Assistant interactions that integrates audio and text as inputs to a Large Language Model (LLM). SELMA is designed to handle three primary and two auxiliary tasks related to interactions with virtual assistants simultaneously within a single end-to-end model. We employ low-rank adaptation modules for parameter-efficient training of both the audio encoder and the LLM. Additionally, we implement a feature pooling strategy enabling the system to recognize global patterns and improve accuracy on tasks less reliant on individual sequence elements. Experimental results on Voice Trigger (VT) detection, Device-Directed Speech Detection (DDSD), and Automatic Speech Recognition (ASR), demonstrate that our approach both simplifies the typical input processing pipeline of virtual assistants significantly and also improves performance compared to dedicated models for each individual task. SELMA yields relative Equal-Error Rate improvements of 64% on the VT detection task, and 22% on DDSD, while also achieving word error rates close to the baseline. KW - multi-task KW - multimodal KW - virtual assistant KW - large language KW - model KW - low-rank adaptation Y1 - 2025 U6 - https://doi.org/10.1109/ICASSP49660.2025.10890139 SN - 2379-190X SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Lee, Seanie A1 - Seong, Haebin A1 - Bok Lee, Dong A1 - Kang, Minki A1 - Chen, Xiaoyin A1 - Wagner, Dominik A1 - Bengio, Joshua A1 - Lee, Juho A1 - Hwang, Sung Ju T1 - HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models N2 - Safety guard models that detect malicious queries aimed at large language models(LLMs) are essential for ensuring the secure and responsible deployment of LLMs in real-world applications. However, deploying existing safety guard models with billions of parameters alongside LLMs on mobile devices is impractical due to substantial memory requirements and latency. To reduce this cost, we distill a large teacher safety guard model into a smaller one using a labeled dataset of instruction-response pairs with binary harmfulness labels. Due to the limited diversity of harmful instructions in the existing labeled dataset, naively distilled models tend to underperform compared to larger models. To bridge the gap between small and large models, we propose HarmAug, a simple yet effective data augmentation method that involves jailbreaking an LLM and prompting it to generate harmful instructions. Given a prompt such as, “Make a single harmful instruction prompt that would elicit offensive content”, we add an affirmative prefix (e.g., “I have an idea for a prompt:”) to the LLM’s response. This encourages the LLM to continue generating the rest of the response, leading to sampling harmful instructions. Another LLM generates a response to the harmful instruction, and the teacher model labels the instruction-response pair. We empirically show that our HarmAug outperforms other relevant baselines. Moreover, a 435-millionparameter safety guard model trained with HarmAug achieves an F1 score comparable to larger models with over 7 billion parameters, and even outperforms them in AUPRC, while operating at less than 25% of their computational cost. Our code, safety guard model, and synthetic dataset are publicly available. Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2410.01524 SP - 1 EP - 20 PB - ICLR ER - TY - CHAP A1 - Braun, Franziska A1 - Witzl, Christopher A1 - Erzigkeit, Andreas A1 - Lehfeld, Hartmut A1 - Hillemacher, Thomas A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Pitfalls and Limits in Automatic Dementia Assessment T2 - Interspeech 2025 N2 - Current work on speech-based dementia assessment focuses on either feature extraction to predict assessment scales, or on the automation of existing test procedures. Most research uses public data unquestioningly and rarely performs a detailed error analysis, focusing primarily on numerical performance. We perform an in-depth analysis of an automated standardized dementia assessment, the Syndrom-Kurz-Test. We find that while there is a high overall correlation with human annotators, due to certain artifacts, we observe high correlations for the severely impaired individuals, which is less true for the healthy or mildly impaired ones. Speech production decreases with cognitive decline, leading to overoptimistic correlations when test scoring relies on word naming. Depending on the test design, fallback handling introduces further biases that favor certain groups. These pitfalls remain independent of group distributions in datasets and require differentiated analysis of target groups. KW - dementia screening KW - neuropsychological tests KW - pathological speech Y1 - 2025 U6 - https://doi.org/10.21437/Interspeech.2025-2280 SN - 2958-1796 SP - 5663 EP - 5667 PB - ISCA ER - TY - CHAP A1 - Bundscherer, Maximilian A1 - Schmitt, Thomas A1 - Baumann, Ilja A1 - Bocklet, Tobias T1 - Digital Operating Mode Classification of Real-World Amateur Radio Transmissions T2 - ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - This study presents an ML approach for classifying digital radio operating modes evaluated on real-world transmissions. We generated 98 different parameterized radio signals from 17 digital operating modes, transmitted each of them on the 70 cm (UHF) amateur radio band, and recorded our transmissions with two different architectures of SDR receivers. Three lightweight ML models were trained exclusively on spectrograms of limited non-transmitted signals with random characters as payloads. This training involved an online data augmentation pipeline to simulate various radio channel impairments. Our best model, EfficientNetB0, achieved an accuracy of 93.80% across the 17 operating modes and 85.47% across all 98 parameterized radio signals, evaluated on our real-world transmissions with Wikipedia articles as payloads. Furthermore, we analyzed the impact of varying signal durations & the number of FFT bins on classification, assessed the effectiveness of our simulated channel impairments, and tested our models across multiple simulated SNRs. KW - Automatic Modulation Classification KW - Amateur KW - Radio KW - Spectrum Monitoring KW - Cognitive Radio KW - Machine Learning Y1 - 2025 U6 - https://doi.org/10.1109/ICASSP49660.2025.10889837 SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Baumann, Ilja A1 - Wagner, Dominik A1 - Riedhammer, Korbinian A1 - Bocklet, Tobias T1 - Optimized Self-supervised Training with BEST-RQ for Speech Recognition T2 - ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - Self-supervised learning has been successfully used for various speech related tasks, including automatic speech recognition. BERT-based Speech pre-Training with Random-projection Quantizer (BEST-RQ) has achieved state-of-the-art results in speech recognition. In this work, we further optimize the BEST-RQ approach using Kullback-Leibler divergence as an additional regularizing loss and multicodebook extension per cluster derived from low-level feature clustering. Preliminary experiments on train-100 split of LibriSpeech result in a relative improvement of 11.2% on test-clean by using multiple codebooks, utilizing a combination of cross-entropy and Kullback-Leibler divergence further reduces the word error rate by 4.5%. The proposed optimizations on full LibriSpeech pre-training and fine-tuning result in relative word error rate improvements of up to 23.8% on test-clean and 30.6% on testother using 6 codebooks. Furthermore, the proposed setup leads to faster convergence in pre-training and fine-tuning and additionally stabilizes the pre-training. KW - self-supervised learning KW - speech recognition KW - conformer KW - BEST-RQ Y1 - 2025 U6 - https://doi.org/10.1109/ICASSP49660.2025.10889362 SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Freisinger, Steffen A1 - Seeberger, Philipp A1 - Ranzenberger, Thomas A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Towards Multi-Level Transcript Segmentation: LoRA Fine-Tuning for Table-of-Contents Generation T2 - Interspeech 2025 N2 - Segmenting speech transcripts into thematic sections benefits both downstream processing and users who depend on written text for accessibility. We introduce a novel approach to hierarchical topic segmentation in transcripts, generating multi-level tables of contents that capture both topic and subtopic boundaries. We compare zero-shot prompting and LoRA fine-tuning on large language models, while also exploring the integration of high-level speech pause features. Evaluations on English meeting recordings and multilingual lecture transcripts (Portuguese, German) show significant improvements over established topic segmentation baselines. Additionally, we adapt a common evaluation measure for multi-level segmentation, taking into account all hierarchical levels within one metric. KW - topic segmentation KW - spoken content segmentation KW - table of contents generation KW - hierarchical segmentation Y1 - 2025 U6 - https://doi.org/10.21437/Interspeech.2025-2792 SN - 2958-1796 SP - 276 EP - 280 PB - ISCA CY - ISCA ER - TY - JOUR A1 - Lee, Seanie A1 - Bok Lee, Dong A1 - Wagner, Dominik A1 - Kang, Minki A1 - Seong, Haebin A1 - Bocklet, Tobias A1 - Lee, Juho A1 - Hwang, Sung Ju T1 - SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models, JF - Findings of the Association for Computational Linguistics: ACL 2025 N2 - Deploying large language models (LLMs) in real-world applications requires robust safety guard models to detect and block harmful user prompts. While large safety guard models achieve strong performance, their computational cost is substantial. To mitigate this, smaller distilled models are used, but they often underperform on “hard” examples where the larger model provides accurate predictions. We observe that many inputs can be reliablyhandled by the smaller model, while only a small fraction require the larger model’s capacity. Motivated by this, we propose SafeRoute, a binary router that distinguishes hard examples from easy ones. Our method selectively applies the larger safety guard model to the data that the router considers hard, improving efficiency while maintaining accuracy compared to solely using the larger safety guard model. Experimental results on multiple benchmark datasets demonstrate that our adaptive model selection significantly enhances the trade-off between computational cost and safety performance, outperforming relevant baselines. Y1 - 2025 UR - https://aclanthology.org/2025.findings-acl.105.pdf PB - Association for Computational Linguistics CY - Vienna, Austria ER - TY - CHAP A1 - Wagner, Dominik A1 - Baumann, Ilja A1 - Engert, Natalie A1 - Lee, Seanie A1 - Nöth, Elmar A1 - Riedhammer, Korbinian A1 - Bocklet, Tobias T1 - Personalized Fine-Tuning with Controllable Synthetic Speech from LLM-Generated Transcripts for Dysarthric Speech Recognition T2 - Interspeech 2025 N2 - In this work, we present our submission to the Speech Accessibility Project challenge for dysarthric speech recognition. We integrate parameter-efficient fine-tuning with latent audio representations to improve an encoder-decoder ASR system. Synthetic training data is generated by fine-tuning Parler-TTS to mimic dysarthric speech, using LLM-generated prompts for corpus-consistent target transcripts. Personalization with x-vectors consistently reduces word error rates (WERs) over non-personalized fine-tuning. AdaLoRA adapters outperform full fine-tuning and standard low-rank adaptation, achieving relative WER reductions of ∼23% and ∼22%, respectively. Further improvements (∼5% WER reduction) come from incorporating wav2vec 2.0-based audio representations. Training with synthetic dysarthric speech yields up to ∼7% relative WER improvement over personalized fine-tuning alone. KW - speech recognition KW - dysarthric speech KW - personalization KW - low-rank adaptation KW - synthetic data Y1 - 2025 U6 - https://doi.org/10.21437/Interspeech.2025-2155 SN - 2958-1796 SP - 3294 EP - 3298 PB - ISCA CY - ISCA ER - TY - CHAP A1 - Baumann, Ilja A1 - Wagner, Dominik A1 - Riedhammer, Korbinian A1 - Bocklet, Tobias T1 - Pathology-Aware Speech Encoding and Data Augmentation for Dysarthric Speech Recognition T2 - Interspeech 2025 N2 - Automatic speech recognition (ASR) for pathologic speech remains a major challenge due to high variability in articulation, phonation, and prosody distortions. In this work, we propose a pathology-aware speech encoder based on BEST-RQ pre-training, which incorporates 46k hours of speech, including pathologic and atypical speech. We continue pre-training for domain adaptation and experiment with etiology-specific codebooks. We achieve a 13.2% relative word error rate (WER) improvement using the pathology-aware speech encoder with etiology-specific continued pre-training. Additionally, we examine the impact of incorporating synthetic and out-of-domain (OOD) data to further enhance ASR performance. Synthetic data reduces WER by up to 8.7%, while OOD data improves WER by 12.2%. Finally, we introduce a semantic similaritybased data augmentation technique to optimize data selection, achieving a WER improvement of up to 9.7% while minimizing the need for additional training data. KW - pathologic speech recognition KW - speech augmentation KW - self-supervised learning Y1 - 2025 U6 - https://doi.org/10.21437/Interspeech.2025-2724 SN - 2958-1796 SP - 3289 EP - 3293 PB - ISCA CY - ISCA ER - TY - CHAP A1 - Wagner, Dominik A1 - Baumann, Ilja A1 - Bocklet, Tobias T1 - Vocoder-Free Non-parallel Conversion of Whispered Speech With Masked Cycle-Consistent Generative Adversarial Networks N2 - Cycle-consistent generative adversarial networks have been widely used in non-parallel voice conversion (VC). Their ability to learn mappings between source and target features without relying on parallel training data eliminates the need for temporal alignments. However, most methods decouple the conversion of acoustic features from synthesizing the audio signal by using separate models for conversion and waveform synthesis. This work unifies conversion and synthesis into a single model, thereby eliminating the need for a separate vocoder. By leveraging cycle-consistent training and a self-supervised auxiliary training task, our model is able to efficiently generate converted high-quality raw audio waveforms. Subjective listening tests showed that our unified approach achieved improvements of up to 6.7% relative to the baseline in whispered VC. Mean opinion score predictions also yielded stable results in conventional VC (between 0.5% and 2.4% relative improvement). KW - voice conversion KW - generative adversarial networks KW - cycle-consistency KW - masking KW - whispered speech Y1 - 2025 SN - 978-3-032-02548-7 U6 - https://doi.org/10.1007/978-3-032-02548-7_20 PB - Springer CY - Cham ER - TY - JOUR A1 - Ranzenberger, Thomas A1 - Baumann, Ilja A1 - Bayerl, Sebastian A1 - Wagner, Dominik A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Evaluation of recognition errors of hybrid and transformer-based ASR systems in German video lectures JF - Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025 - Book N2 - We analyze different errors in speech recognition systems, focusing on consecutive insertions and deletions, known as hallucinations and elisions in transformer-based end-to-end automatic speech recognition (ASR) systems. We compare errors from a TDNN-HMM, and whisper-based models on English and German spontaneous speech. Based on a human annotated subset of German lecture videos, we investigate whether these blocks of deletions affect the semantics of the utterance. Whisper performs best and preserves the meaning in 90% of the annotated error segments even containing consecutive deletions on this subset. We analyze the word error rate and do further analysis of errors using natural language processing to detect lemmatization errors, compound word errors, and out-of-vocabulary words. We discuss possible reasons and mitigations. Y1 - 2025 UR - https://www.essv.de/pdf/2025_101_108.pdf PB - ESSV 2025 CY - Halle, Deutschland ER - TY - CHAP A1 - Steigerwald, Philipp A1 - Albrecht, Jens T1 - Enhancing Psychosocial Counselling with AI: A Multifaceted Support System for Professionals T2 - Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications N2 - The expanding field of psychosocial online email counselling has created a demand for advanced tools to support counsellors in managing diverse client interactions. This paper introduces CAIA (Counsellor Artificial Intelligence Assistant), a comprehensive AI-driven system currently under active development, designed to condense and present information to counsellors in a structured manner. CAIA integrates several AI-based features, including Automatic Subject Generation, Case Summarisation, Timeline Management, Social Network Graph creation, and Case Classification. The features are designed to assist the counsellors, allowing them to engage more deeply with each case while ensuring that the client's needs remain paramount. Each feature presented in the paper addresses a specific problem in psychosocial online counselling, proposes a solution, discusses a technical implementation approach, and highlights the challenges encountered. Additionally, the paper discusses ethical considerations associated with an AI integration. Y1 - 2025 SN - 9789819679447 U6 - https://doi.org/10.1007/978-981-96-7945-4_35 SN - 2731-8125 SP - 597 EP - 611 PB - Springer Nature CY - Singapore ER - TY - CHAP A1 - Steigerwald, Philipp A1 - Albrecht, Jens T1 - Comparing Large Language Models for Automated Subject Line Generation in e-Mental Health: A Performance Study T2 - Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health N2 - Large Language Models (LLMs) have the potential to enhance e-mental health and psychosocial e-mail counselling by automating tasks such as generating concise and relevant subject lines for client communications. However, concerns regarding accuracy, reliability, data privacy and resource efficiency persist. This study investigates the performance of several LLMs in generating subject lines for e-mail threads, yielding a total of 253 generated subjects. Each subject line was assessed by six raters, including five counselling professionals and one AI system, using a three-category quality scale (Good, Fair, Poor). The results show that LLMs can generally produce concise subject lines considered helpful by experts. While GPT-4o and GPT-3.5 Turbo outperformed other models, their use is restricted in mental health settings due to data protection concerns, making the evaluation of open-source models crucial. Among open-source models, SauerkrautLM LLama 3 70b (4-bit) and SauerkrautLM Mixtral 8x7b (both 8-bit and 4-bit versions) delivered promising results with potential for further development. In contrast, models with lower parameter counts produced predominantly poor outputs. Y1 - 2025 U6 - https://doi.org/10.5220/0013294100003938 VL - 2025 SP - 70 EP - 77 PB - SCITEPRESS - Science and Technology Publications ER - TY - CHAP A1 - Steigerwald, Philipp A1 - Bienlein, Nico A1 - Burghardt, Jennifer A1 - Stieler, Mara A1 - Lehmann, Robert A1 - Albrecht, Jens T1 - CAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online Counselling T2 - 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI) N2 - Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a crucial low-threshold channel for accessing care. This paper presents CAIA, a co-designed AIbased tool suite that demonstrates responsible AI integration into counselling practice through seven LLM-driven functions enhanced by retrieval-augmented generation. A field evaluation involved 34 professional counsellors conducting authentic sessions with trained student counsellees (36 threads, 321 messages, 1,257 AI outputs). User behaviour analysis confirms substantial adoption, revealing that professional autonomy and information accuracy are decisive for sustained acceptance, with counsellors particularly valuing interpretive functionalities that provide new perspectives and stimulate professional reflection. KW - AI-Assisted Counselling KW - Email Counselling KW - Large Language Models KW - Retrieval-Augmented Generation KW - User KW - Adoption Study KW - Human-AI Collaboration Y1 - 2025 U6 - https://doi.org/10.1109/ICTAI66417.2025.00214 SP - 1476 EP - 1483 PB - IEEE ER - TY - CHAP A1 - Burghardt, Jennifer A1 - Stieler, Mara A1 - Steigerwald, Philipp A1 - Rudolph, Eric A1 - Albrecht, Jens A1 - Lehmann, Robert T1 - KI-gestützte Beratung – Assistenzsysteme für Ratsuchende und Fachkräfte in der digitalen Beratung T2 - Deutscher Suchtkongress (DSK) Y1 - 2025 U6 - https://doi.org/10.18416/DSK.2025.2259 VL - 2 IS - 1 ER -