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. 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.

V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 1, pp. 95–109, 2014.

J. Li, L. Deng, Y. Gong, and R. Haeb-Umbach, “An Overview of Noise-Robust Automatic Speech Recognition,” IEEE Transactions on Audio, Speech and Language Processing, vol. 22, no. 4, pp. 745–777, 2014.

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

V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 8, pp. 1640–1652, 2013.

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

A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 7, pp. 1692–1707, 2010.

S. Windmann and R. Haeb-Umbach, “Approaches to Iterative Speech Feature Enhancement and Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 5, pp. 974–984, 2009.

S. Windmann and R. Haeb-Umbach, “Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 8, pp. 1577–1590, 2009.

V. Ion and R. Haeb-Umbach, “A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 5, pp. 1047–1060, 2008.

V. Ion and R. Haeb-Umbach, “An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, vol. 1, p. I.

V. Ion and R. Haeb-Umbach, “Uncertainty decoding for distributed speech recognition over error-prone networks,” Speech Communication, vol. 48, no. 11, pp. 1435–1446, 2006.

V. Ion and R. Haeb-Umbach, “A Comparison of Soft-Feature Distributed Speech Recognition with Candidate Codecs for Speech Enabled Mobile Services,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005), 2005, vol. 1, pp. 333–336.

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

W. Müller, R. Schäfer, and S. Bleul, “Interactive Multimodal User Interfaces for Mobile Devices,”10.1109/HICSS.2004.1265674__presented at the 37th Annual Hawaii International Conference on System Sciences, Waikoloa, HI, USA, 2004, doi:10.1109/HICSS.2004.1265674 .

R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for MLLR adaptation,” IEEE Transactions on Speech and Audio Processing, vol. 9, no. 3, pp. 299–302, 2001.

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