@article{FreierBockletHeltenetal.2023, author = {Freier, Carolin and Bocklet, Tobias and Helten, Anne-Kathrin and Hoffmann, Franziska and Hunger, Marianne and Kov{\´a}cs, L{\´a}szl{\´o} and Richter, Florian and Riedhammer, Korbinian and Schmohl, Tobias and Simon, Claudia}, title = {Wie kann videogest{\"u}tztes Lernen die Erwartungen Studierender und Dozierender erf{\"u}llen?}, series = {Soziale Passagen}, volume = {15}, journal = {Soziale Passagen}, number = {2}, publisher = {Springer VS}, issn = {1867-0180}, doi = {10.1007/s12592-023-00478-0}, pages = {631 -- 635}, year = {2023}, abstract = {ZusammenfassungIm BMBF-Verbundprojekt HAnS entwickeln und implementieren neun Hochschulen sowie drei hochschul{\"u}bergreifende Einrichtungen ein intelligentes Hochschul-Assistenz-System als Open-Source-L{\"o}sung. Videobasierte Lehrmaterialien werden verschriftlicht und durch eine Indexierung Stichwortsuchen erm{\"o}glicht; geplant ist, {\"u}ber einen KI-Tutor automatisiert {\"U}bungsaufgaben zu generieren. Studierende sollen so in ihrem Selbststudium digital unterst{\"u}tzt werden. Die technische Entwicklung wird interdisziplin{\"a}r - auch sozialwissenschaftlich und p{\"a}dagogisch - begleitet und in einem iterativen Vorgehen evidenzbasiert entsprechend Design-Based-Research angepasst. Wissen und Wertesystem der Anwender*innen, Didaktik, Ethik, Akzeptanz und Datenschutz werden dabei im Entwicklungsprozess einbezogen.}, language = {de} } @article{SchusterAriasVergaraMuellerHoerneretal.2020, author = {Schuster, Maria and Arias-Vergara, Tomas and M{\"u}ller-H{\"o}rner, Rainer and Winterholler, Cordula and Bocklet, Tobias}, title = {Verstehen mich mit der Maske noch alle?}, series = {MMW - Fortschritte der Medizin}, volume = {162}, journal = {MMW - Fortschritte der Medizin}, number = {14}, doi = {10.1007/s15006-020-0749-4}, pages = {42 -- 44}, year = {2020}, abstract = {In Arztpraxen nimmt der Infektionsschutz mittels Mund-Nasen-Maske momentan eine zentrale Rolle ein. Wenig nachgedacht wird dabei {\"u}ber die Folgen f{\"u}r die Kommunikation. Gerade schwerh{\"o}rigen Patienten bereiten ged{\"a}mpfte Stimmen und abgedeckte Lippen Probleme.}, language = {de} } @article{BockletNoethRiedhammer2023, author = {Bocklet, Tobias and N{\"o}th, Elmar and Riedhammer, Korbinian}, title = {K{\"u}nstliche Intelligenz f{\"u}r die Analyse pathologischer Sprache}, series = {Sprache - Stimme - Geh{\"o}r}, volume = {47}, journal = {Sprache - Stimme - Geh{\"o}r}, number = {3}, doi = {10.1055/a-2089-5778}, pages = {145-150}, year = {2023}, abstract = {Sprache kann eine Vielzahl von diagnostisch relevanten Informationen enthalten. In diesem {\"U}bersichtsartikel wird aufgezeigt, wie Methoden der K{\"u}nstlichen Intelligenz, insbesondere Maschinelles Lernen und Sprachverarbeitung, angewendet auf Sprachsignale eingesetzt werden k{\"o}nnen: zur Bewertung von Verst{\"a}ndlichkeit, zur Automatisierung von standardisierten Tests und zur Bestimmung medizinischer Skalen und Diagnosen. Eine abschließende kritischen Betrachtung von akustischen Merkmalen {\"u}ber eine Vielzahl von Pathologien gibt Grund zur Annahme, dass diese Marker tats{\"a}chlich diagnostisch relevante Informationen enthalten.}, language = {de} } @inproceedings{BraunBayerlPerezToroetal.2023, author = {Braun, Franziska and Bayerl, Sebastian P. and P{\´e}rez-Toro, Paula A. and H{\"o}nig, Florian and Lehfeld, Hartmut and Hillemacher, Thomas and N{\"o}th, Elmar and Bocklet, Tobias and Riedhammer, Korbinian}, title = {Classifying Dementia in the Presence of Depression}, doi = {10.48550/arXiv.2308.08306}, pages = {5}, year = {2023}, abstract = {Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn.}, language = {en} } @inproceedings{RiedhammerBaumannBayerletal.2023, author = {Riedhammer, Korbinian and Baumann, Ilja and Bayerl, Sebastian P. and Bocklet, Tobias and Braun, Franziska and Wagner', Dominik}, title = {Medical Speech Processing for Diagnosis and Monitoring}, pages = {1417 -- 1420}, year = {2023}, abstract = {In recent years, speech processing for medical applications got significant traction. While pioneering work in the 1990ies focused on processing sustained vowels or isolated utterances, work in the 2000s already showed, that speech recognition systems, prosodic analysis and natural language processing be used to assess a large variety of speech pathologies.Here, we give an overview of how to classify selected speech pathologies including stuttering, language development, speech intelligibility after surgery, dementia and Alzheimers, depression and state-of-mind. While each of those poses a rather well-defined problem in a lab setting, we discuss the issues when integrating such methods in a clinical workflow such as diagnosis or monitoring. Starting from the question if such detectors can be used for general screening or rather as a specialist's tool, we explore the legal and privacy-related implications: patient-doctor conversations, working with children or demented seniors, bias towards examiner or patient, on-device vs. cloud processing.We conclude with a set of open questions that should be addressed to help bringing all this research from the lab to routine clinical use.}, language = {en} } @inproceedings{WagnerBaumannBraunetal.2023, author = {Wagner, Dominik and Baumann, Ilja and Braun, Franziska and Bayerl, Sebastian P. and N{\"o}th, Elmar and Riedhammer, Korbinian and Bocklet, Tobias}, title = {Multi-class Detection of Pathological Speech with Latent Features}, issn = {2958-1796}, doi = {10.21437/Interspeech.2023-464}, pages = {2318 -- 2322}, year = {2023}, abstract = {The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech. In this work, we train neural networks, large margin classifiers, and tree boosting machines to distinguish between four pathologies: Parkinson's disease, laryngeal cancer, cleft lip and palate, and oral squamous cell carcinoma. We show that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be effectively used to classify these types of pathological voices. We evaluate the robustness of our classifiers by adding room impulse responses to the test data and by applying them to unseen speech corpora. Our approach achieves unweighted average F1-Scores between 74.1\% and 97.0\%, depending on the model and the noise conditions used. The systems generalize and perform well on unseen data of healthy speakers sampled from a variety of different sources.}, language = {en} } @inproceedings{ChenHuangBocklet2020, author = {Chen, Wenda and Huang, Jonathan and Bocklet, Tobias}, title = {Length-and Noise-aware Training Techniques for Short-utterance Speaker Recognition}, doi = {10.48550/arXiv.2008.12218}, pages = {5}, year = {2020}, abstract = {Speaker recognition performance has been greatly improved with the emergence of deep learning. Deep neural networks show the capacity to effectively deal with impacts of noise and reverberation, making them attractive to far-field speaker recognition systems. The x-vector framework is a popular choice for generating speaker embeddings in recent literature due to its robust training mechanism and excellent performance in various test sets. In this paper, we start with early work on including invariant representation learning (IRL) to the loss function and modify the approach with centroid alignment (CA) and length variability cost (LVC) techniques to further improve robustness in noisy, far-field applications. This work mainly focuses on improvements for short-duration test utterances (1-8s). We also present improved results on long-duration tasks. In addition, this work discusses a novel self-attention mechanism. On the VOiCES far-field corpus, the combination of the proposed techniques achieves relative improvements of 7.0\% for extremely short and 8.2\% for full-duration test utterances on equal error rate (EER) over our baseline system.}, language = {en} } @inproceedings{WagnerBaumannBayerletal.2023, author = {Wagner, Dominik and Baumann, Ilja and Bayerl, Sebastian P. and Riedhammer, Korbinian and Bocklet, Tobias}, title = {Speaker Adaptation for End-To-End Speech Recognition Systems in Noisy Environments}, doi = {10.48550/arXiv.2211.08774}, pages = {6}, year = {2023}, abstract = {We analyze the impact of speaker adaptation in end-to-end automatic speech recognition models based on transformers and wav2vec 2.0 under different noise conditions. By including speaker embeddings obtained from x-vector and ECAPA-TDNN systems, as well as i-vectors, we achieve relative word error rate improvements of up to 16.3\% on LibriSpeech and up to 14.5\% on Switchboard. We show that the proven method of concatenating speaker vectors to the acoustic features and supplying them as auxiliary model inputs remains a viable option to increase the robustness of end-to-end architectures. The effect on transformer models is stronger, when more noise is added to the input speech. The most substantial benefits for systems based on wav2vec 2.0 are achieved under moderate or no noise conditions. Both x-vectors and ECAPA-TDNN embeddings outperform i-vectors as speaker representations. The optimal embedding size depends on the dataset and also varies with the noise condition.}, language = {en} } @inproceedings{WagnerBayerlBockletetal.2023, author = {Wagner, Dominik and Bayerl, Sebastian P. and Bocklet, Tobias and Draxler, Christoph}, title = {Implementing Easy-to-Use Recipes for the Switchboard Benchmark}, publisher = {TUDpress, Dresden}, pages = {150 -- 157}, year = {2023}, abstract = {We report on our contribution of templates for tokenization, language modeling, and automatic speech recognition (ASR) on the Switchboard benchmark to the open-source general-purpose toolkit SpeechBrain. Three recipes for the training of end-to-end ASR systems were implemented. We describe their model architectures, as well as the necessary data preparation steps. The word error rates achievable with our models are comparable to or better than those of other popular toolkits. Pre-trained ASR models were made available on HuggingFace. They can be easily integrated into research projects or used directly for quick inference via a hosted inference API.}, language = {en} } @inproceedings{BaumannWagnerRiedhammeretal.2023, author = {Baumann, Ilja and Wagner, Dominik and Riedhammer, Korbinian and N{\"o}th, Elmar and Bocklet, Tobias}, title = {Detection of Vowel Errors in Children's Speech Using Synthetic Phonetic Transcripts}, series = {2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Taipei, Taiwan, 2023}, booktitle = {2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Taipei, Taiwan, 2023}, doi = {10.1109/ASRU57964.2023.10389704}, pages = {1 -- 8}, year = {2023}, abstract = {The analysis of phonological processes is crucial in evaluating speech development disorders in children, but encounters challenges due to limited children audio data. This work focuses on automatic vowel error detection using a two-stage pipeline. The first stage uses a fine-tuned cross-lingual phone recognizer (wav2vec 2.0) to extract phone sequences from audio. The second stage employs a language model (BERT) for classification from a phone sequence, entirely trained on synthetic transcripts, to counteract the very broad range of potential mistakes. We evaluate the system on nonword audio recordings recited by preschool children from a speech development test. The results show that the classifier trained on synthetic data performs well, but its efficacy relies on the quality of the phone recognizer. The best classifier achieves an 94.7\% F1 score when evaluated against phonetic ground truths, whereas the F1 score is 76.2\% when using automatically recognized phone sequences.}, language = {en} }