Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Bayerl, Sebastian P"'
Autor:
Braun, Franziska, Bayerl, Sebastian P., Hönig, Florian, Lehfeld, Hartmut, Hillemacher, Thomas, Bocklet, Tobias, Riedhammer, Korbinian
Speech pauses, alongside content and structure, offer a valuable and non-invasive biomarker for detecting dementia. This work investigates the use of pause-enriched transcripts in transformer-based language models to differentiate the cognitive state
Externí odkaz:
http://arxiv.org/abs/2408.15188
Autor:
Wagner, Dominik, Bayerl, Sebastian P., Baumann, Ilja, Riedhammer, Korbinian, Nöth, Elmar, Bocklet, Tobias
Accurately detecting dysfluencies in spoken language can help to improve the performance of automatic speech and language processing components and support the development of more inclusive speech and language technologies. Inspired by the recent tre
Externí odkaz:
http://arxiv.org/abs/2406.11025
Autor:
Braun, Franziska, Bayerl, Sebastian P., Pérez-Toro, Paula A., Hönig, Florian, Lehfeld, Hartmut, Hillemacher, Thomas, Nöth, Elmar, Bocklet, Tobias, Riedhammer, Korbinian
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
Externí odkaz:
http://arxiv.org/abs/2308.08306
Autor:
Bayerl, Sebastian P., Wagner, Dominik, Baumann, Ilja, Hönig, Florian, Bocklet, Tobias, Nöth, Elmar, Riedhammer, Korbinian
Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency se
Externí odkaz:
http://arxiv.org/abs/2305.19255
This work adapts two recent architectures of generative models and evaluates their effectiveness for the conversion of whispered speech to normal speech. We incorporate the normal target speech into the training criterion of vector-quantized variatio
Externí odkaz:
http://arxiv.org/abs/2212.01775
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 we
Externí odkaz:
http://arxiv.org/abs/2211.08774
Autor:
Bayerl, Sebastian P., Wagner, Dominik, Hönig, Florian, Bocklet, Tobias, Nöth, Elmar, Riedhammer, Korbinian
Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent works have treated stuttering as a multi-class classification problem
Externí odkaz:
http://arxiv.org/abs/2210.15982
Autor:
Baumann, Ilja, Wagner, Dominik, Braun, Franziska, Bayerl, Sebastian P., Nöth, Elmar, Riedhammer, Korbinian, Bocklet, Tobias
Recent findings show that pre-trained wav2vec 2.0 models are reliable feature extractors for various speaker characteristics classification tasks. We show that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system c
Externí odkaz:
http://arxiv.org/abs/2210.15941
Autor:
Wagner, Dominik, Baumann, Ilja, Braun, Franziska, Bayerl, Sebastian P., Nöth, Elmar, Riedhammer, Korbinian, Bocklet, Tobias
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 margi
Externí odkaz:
http://arxiv.org/abs/2210.15336
Autor:
Bundscherer, Maximilian, Schmitt, Thomas H., Bayerl, Sebastian, Auerbach, Thomas, Bocklet, Tobias
This paper describes a machine learning approach to determine the abrasive belt wear of wide belt sanders used in industrial processes based on acoustic data, regardless of the sanding process-related parameters, Feed speed, Grit Size, and Type of ma
Externí odkaz:
http://arxiv.org/abs/2210.13273