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.

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.

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.

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.

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.

Model-Based Feature Enhancement for Reverberant Speech Recognition
A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 18 (2010) 1692–1707.

Approaches to Iterative Speech Feature Enhancement and Recognition
S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 974–984.

Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition
S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 1577–1590.

A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition
V. Ion, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 16 (2008) 1047–1060.

An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features
V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.

Uncertainty decoding for distributed speech recognition over error-prone networks
V. Ion, R. Haeb-Umbach, Speech Communication 48 (2006) 1435–1446.

A Comparison of Soft-Feature Distributed Speech Recognition with Candidate Codecs for Speech Enabled Mobile Services
V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005), 2005, pp. 333–336.

Robust speaker direction estimation with particle filtering
E. Warsitz, R. Haeb-Umbach, in: IEEE Workshop on Multimedia Signal Processing (MMSP 2004), 2004, pp. 367–370.

Interactive Multimodal User Interfaces for Mobile Devices
W. Müller, R. Schäfer, S. Bleul, in: Proceedings of HICCS-37, Waikoloa, HI, USA, 2004.

Automatic generation of phonetic regression class trees for MLLR adaptation
R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001) 299–302.

LDA derived cepstral trajectory filters in adverse environmental conditions
M. Lieb, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2000), 2000, pp. - vol.2.