2023

  • [1] P. A. Pérez-Toro et al., “Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer’s Disease Using Forestnets,” in ICASSP 2023, Rhodes Island, Greece, 2023, pp. 1–5, doi: 10.1109/ICASSP49357.2023.10095219.
  • [2] J. Hintz et al., “Anonymization of Stuttered Speech – Removing Speaker Information While Preserving the Utterance,” in ISCA SPSC, Dublin, 2023.
  • [3] J. Hintz, Y. Sinha, S. P. Bayerl, K. Riedhammer, and I. Siegert, “Impact of Pathological Speech on Speaker Anonymization - A Proof of Concept,” in DAGA, Hamburg, 2023.
  • [4] S. P. Bayerl et al., “A Stutter Seldom Comes Alone – Cross-Corpus Stuttering Detection as a Multi-Label Problem,” in Proc. INTERSPEECH 2023, Dublin, 2023, pp. 1538–1542, doi: 10.21437/Interspeech.2023-2026.
  • [5] S. P. Bayerl, D. Wagner, I. Baumann, T. Bocklet, and K. Riedhammer, “Detecting Vocal Fatigue with Neural Embeddings,” Journal of Voice, 2023, doi: 10.1016/j.jvoice.2023.01.012.
  • [6] S. P. Bayerl et al., “Classification of Stuttering – The ComParE Challenge and Beyond,” Computer Speech & Language, vol. 81, p. 101519, Jun. 2023, doi: 10.1016/j.csl.2023.101519.
  • [7] I. Baumann, S. P. Bayerl, T. Bocklet, F. Braun, K. Riedhammer, and D. Wagner, “Medical Speech Processing for Diagnosis and Monitoring: Clinical Use Cases,” in DAGA, Hamburg, 2023.

2022

  • [1] S. P. Bayerl, D. Wagner, F. Hönig, T. Bocklet, E. Nöth, and K. Riedhammer, “Dysfluencies Seldom Come Alone – Detection as a Multi-Label Problem,” no. arXiv:2210.15982. arXiv, Oct-2022 [Online]. Available at: http://arxiv.org/abs/2210.15982
  • [2] B. Schuller et al., “The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes,” in Proceedings of the 30th ACM International Conference on Multimedia, New York, NY, USA, 2022, pp. 7120–7124, doi: 10.1145/3503161.3551591.
  • [3] I. Baumann, D. Wagner, S. P. Bayerl, and T. Bocklet, “Nonwords Pronunciation Classification in Language Development Tests for Preschool Children,” in Proc. Interspeech 2022, 2022.
  • [4] S. P. Bayerl, D. Wagner, E. Nöth, and K. Riedhammer, “Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0,” in Proc. Interspeech 2022, 2022 [Online]. Available at: https://arxiv.org/abs/2204.03417
  • [5] S. P. Bayerl, D. Wagner, E. Nöth, T. Bocklet, and K. Riedhammer, “The Influence of Dataset Partitioning on Dysfluency Detection Systems,” in Text, Speech, and Dialogue, Springer International Publishing, 2022 [Online]. Available at: https://arxiv.org/abs/2206.03400
  • [6] S. P. Bayerl, A. Wolff von Gudenberg, F. Hönig, E. Nöth, and K. Riedhammer, “KSoF: The Kassel State of Fluency Dataset – A Therapy Centered Dataset of Stuttering,” in Proceedings of the Language Resources and Evaluation Conference, Marseille, France, 2022, pp. 1780–1787 [Online]. Available at: https://arxiv.org/abs/2203.05383
  • [7] S. P. Bayerl et al., “What Can Speech and Language Tell Us About the Working Alliance in Psychotherapy,” in Proc. Interspeech 2022, 2022, pp. 2443–2447, doi: 10.21437/Interspeech.2022-347.
  • [8] F. Braun, A. Erzigkeit, H. Lehfeld, T. Hillemacher, K. Riedhammer, and S. P. Bayerl, “Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments Using Acoustic Features,” in Text, Speech, and Dialogue, Springer International Publishing, 2022 [Online]. Available at: https://arxiv.org/abs/2206.05018
  • [9] A. Tammewar, F. Braun, G. Roccabruna, S. P. Bayerl, K. Riedhammer, and G. Riccardi, “Annotation of Valence Unfolding in Spoken Personal Narratives,” in Proceedings of the Language Resources and Evaluation Conference, Marseille, France, 2022, pp. 7004–7013.

2021

  • [1] S. P. Bayerl, A. Tammewar, K. Riedhammer, and G. Riccardi, “Detecting Emotion Carriers by Combining Acoustic and Lexical Representations,” in 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2021, pp. 31–38, doi: 10.1109/ASRU51503.2021.9687893 [Online]. Available at: https://arxiv.org/abs/2112.06603
  • [2] S. P. Bayerl, M. Wenninger, J. Schmidt, A. W. von Gudenberg, and K. Riedhammer, “STAN: A Stuttering Therapy Analysis Helper,” in Demo, IEEEE Spoken Language Technology Workshop (SLT), 2021 [Online]. Available at: https://rc.signalprocessingsociety.org/workshops/slt-2021/SLT21VID155.html?source=IBP
  • [3] P. Klumpp et al., “The Phonetic Footprint of Covid-19,” Proc. Interspeech 2021, 2021.
  • [4] P. A. Pérez-Toro et al., “Influence of the Interviewer on the Automatic Assessment of Alzheimer’s Disease in the Context of the ADReSSo Challenge,” in Proc. Interspeech 2021, 2021, pp. 3785–3789.
  • [5] M. Wenninger, S. P. Bayerl, A. Maier, and J. Schmidt, “Recurrence Plot Spacial Pyramid Pooling Network for Appliance Identification in Non-Intrusive Load Monitoring,” in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021, pp. 108–115.

2020

  • [1] S. P. Bayerl, F. Hönig, J. Reister, and K. Riedhammer, “Towards Automated Assessment of Stuttering and Stuttering Therapy,” in Text, Speech, and Dialogue, Cham, 2020, vol. 12284, pp. 386–396, doi: 10.1007/978-3-030-58323-1_42 [Online]. Available at: https://arxiv.org/abs/2006.09222
  • [2] S. P. Bayerl et al., “Offline Model Guard: Secure and Private ML on Mobile Devices,” in 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020, pp. 460–465.
  • [3] J. R. Orozco-Arroyave et al., “Apkinson: The Smartphone Application for Telemonitoring Parkinson’s Patients through Speech, Gait and Hands Movement,” Neurodegenerative Disease Management, vol. 10, no. 3, pp. 137–157, 2020.

2019

  • [1] S. P. Bayerl and K. Riedhammer, “A Comparison of Hybrid and End-to-End Models for Syllable Recognition,” in International Conference on Text, Speech, and Dialogue, 2019, pp. 352–360 [Online]. Available at: https://arxiv.org/abs/1909.12232
  • [2] S. P. Bayerl et al., “Privacy-Preserving Speech Processing via STPC and TEEs,” 2019.
  • [3] J. C. Vásquez-Correa et al., “Apkinson: A Mobile Solution for Multimodal Assessment of Patients with Parkinson’s Disease.,” in INTERSPEECH, 2019, pp. 964–965.
  • [4] M. Wenninger, S. P. Bayerl, J. Schmidt, and K. Riedhammer, “Timage–a Robust Time Series Classification Pipeline,” in International Conference on Artificial Neural Networks, 2019, pp. 450–461 [Online]. Available at: https://arxiv.org/abs/1909.09149