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.

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.

K. Kinoshita et al., “The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech,” in  IEEE Workshop on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.

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.

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

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

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, E. Warsitz, and R. Haeb-Umbach, “Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 1, pp. 206–219, 2011.

K. Fischer, K. Foth, K. Rohlfing, and B. Wrede, “Mindful tutors: Linguistic choice and action demonstration in speech to infants and a simulated robot,”10.1075/is.12.1.06fis__Interaction Studies, vol. 12, no. 1, pp. 134–161, 2011, doi:10.1075/is.12.1.06fis .

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.

D. H. Tran Vu and R. Haeb-Umbach, “Blind speech separation employing directional statistics in an Expectation Maximization framework,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 2010, pp. 241–244.

J. Schmalenstroeer and R. Haeb-Umbach, “Online Diarization of Streaming Audio-Visual Data for Smart Environments,”10.1109/JSTSP.2010.2050519__IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 5, pp. 845–856, 2010, doi:10.1109/JSTSP.2010.2050519 .

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.

A.-L. Vollmer et al., “People modify their tutoring behavior in robot-directed interaction for action learning,”10.1109/DEVLRN.2009.5175516__in Development and Learning, 2009. ICDL 2009. IEEE 8th International Conference on Development and Learning, 2009, pp. 1–6, doi:10.1109/DEVLRN.2009.5175516 .

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.

E. Warsitz, A. Krueger, and R. Haeb-Umbach, “Speech enhancement with a new generalized eigenvector blocking matrix for application in a generalized sidelobe canceller,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 73–76.

S. Windmann and R. Haeb-Umbach, “Modeling the dynamics of speech and noise for speech feature enhancement in ASR,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–4412.

M. Lohse, K. Rohlfing, B. Wrede, and G. Sagerer, “‘Try something else!’ — When users change their discursive behavior in human-robot interaction,”10.1109/ROBOT.2008.4543743__2008, pp. 3481–3486, doi:10.1109/ROBOT.2008.4543743 .

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.

S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, vol. 1, p. I.

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.

C. Plessl and S. Maurer, Hardware/Software Codesign in Speech Compression Applications. Computer Engineering and Networks Lab, ETH Zurich, Switzerland, 2000.

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.