Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria
T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, R. Haeb-Umbach, IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (2023) 576–589.

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

Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers
T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, R. Haeb-Umbach, in: Interspeech 2021, 2021.

Improving the efficacy and reliability of rTMS language mapping by increasing the stimulation frequency
C. Nettekoven, J. Pieczewski, V. Neuschmelting, K. Jonas, R. Goldbrunner, C. Grefkes, C. Weiss Lucas, Human Brain Mapping 42 (2021) 5309–5321.

Demystifying TasNet: A Dissecting Approach
J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, R. Haeb-Umbach, in: ICASSP 2020 Virtual Barcelona Spain, 2020.

Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments
J. Heitkaemper, J. Schmalenstroeer, R. Haeb-Umbach, in: INTERSPEECH 2020 Virtual Shanghai China, 2020.

Towards a Computational Model of Child Gesture-Speech Production
O. Abramov, S. Kopp, A. Nemeth, F. Kern, U. Mertens, K. Rohlfing, in: KOGW: Computational Approaches to Cognitive Science, 2018.

Towards a Computational Model of Child Gesture-Speech Production
O. Abramov, S. Kopp, A. Nemeth, F. Kern, U. Mertens, K. Rohlfing, in: KOGW: Computational Approaches to Cognitive Science, 2018.

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

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

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

A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech
V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE/ACM Transactions on Audio, Speech, and Language Processing 22 (2014) 95–109.

An Overview of Noise-Robust Automatic Speech Recognition
J. Li, L. Deng, Y. Gong, R. Haeb-Umbach, IEEE Transactions on Audio, Speech and Language Processing 22 (2014) 745–777.

GMM-based significance decoding
A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On, 2013, pp. 6827–6831.

The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech
K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, E. Habets, R. Haeb-Umbach, V. Leutnant, A. Sehr, W. Kellermann, R. Maas, S. Gannot, B. Raj, in:  IEEE Workshop on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.

Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition
V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 21 (2013) 1640–1652.

Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation
D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.

Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor
A. Chinaev, A. Krueger, D.H. Tran Vu, R. Haeb-Umbach, in: 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.

A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR
V. Leutnant, A. Krueger, R. Haeb-Umbach, in: Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference On, 2012.

Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation
A. Krueger, E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 19 (2011) 206–219.