2022
- [1] 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.
- [2] 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
- [3] 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
- [4] 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
- [5] S. P. Bayerl et al., “What Can Speech and Language Tell Us About the Working Alliance in Psychotherapy,” in Proc. Interspeech 2022, 2022 [Online]. Available at: https://arxiv.org/abs/2206.08835
- [6] 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
- [7] 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.
- [8] B. W. Schuller et al., “The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes,” arXiv preprint arXiv:2205.06799, 2022.
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