T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria,”10.1109/taslp.2022.3228629__IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 576–589, 2023, doi:10.1109/taslp.2022.3228629 .

T. von Neumann, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems,” presented at the CHiME 2023 Workshop on Speech Processing in Everyday Environments, Dublin, 2023.

T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers,”10.21437/interspeech.2021-1177__presented at the Interspeech, 2021, doi:10.21437/interspeech.2021-1177 .

J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, and R. Haeb-Umbach, “Demystifying TasNet: A Dissecting Approach,” 2020.

J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments,” 2020.

O. Abramov, S. Kopp, A. Nemeth, F. Kern, U. Mertens, and K. Rohlfing, “Towards a Computational Model of Child Gesture-Speech Production,” 2018.

O. Abramov, S. Kopp, A. Nemeth, F. Kern, U. Mertens, and K. Rohlfing, “Towards a Computational Model of Child Gesture-Speech Production,” 2018.

A. Chinaev and R. Haeb-Umbach, “On Optimal Smoothing in Minimum Statistics Based Noise Tracking,” in Interspeech 2015, 2015, pp. 1785–1789.

J. Heymann, R. Haeb-Umbach, P. Golik, and R. Schlueter, “Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions,” in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015, pp. 5053–5057.

L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models,” in 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.