Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation
L. Drude, R. Haeb-Umbach, IEEE Journal of Selected Topics in Signal Processing (2019).

Improving CTC Using Stimulated Learning for Sequence Modeling
J. Heymann, B.L. Khe Chai Sim, in: ICASSP 2019, Brighton, UK, 2019.

An Investigation Into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party Transcription
C. Zorila, C. Boeddeker, R. Doddipatla, R. Haeb-Umbach, in: ASRU 2019, Sentosa, Singapore, 2019.

A Study on Online Source Extraction in the Presence of Changing Speaker Positions
J. Heitkaemper, T. Feher, M. Freitag, R. Haeb-Umbach, in: International Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia, 2019.

Multi-Channel Block-Online Source Extraction based on Utterance Adaptation
J.M. Martin-Donas, J. Heitkaemper, R. Haeb-Umbach, A.M. Gomez, A.M. Peinado, in: INTERSPEECH 2019, Graz, Austria, 2019.

Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
N. Kanda, C. Boeddeker, J. Heitkaemper, Y. Fujita, S. Horiguchi, R. Haeb-Umbach, in: INTERSPEECH 2019, Graz, Austria, 2019.

All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis
T. von Neumann, K. Kinoshita, M. Delcroix, S. Araki, T. Nakatani, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK, 2019.

Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques
R. Haeb-Umbach, S. Watanabe, T. Nakatani, M. Bacchiani, B. Hoffmeister, M.L. Seltzer, H. Zen, M. Souden, IEEE Signal Processing Magazine 36 (2019) 111–124.

Lektionen für Alexa & Co?!
R. Haeb-Umbach, DFG Forschung 1/2019 (2019) 12–15.

Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion
T. Gburrek, T. Glarner, J. Ebbers, R. Haeb-Umbach, P. Wagner, in: Proc. 10th ISCA Speech Synthesis Workshop, 2019, pp. 81–86.

Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision
J. Ebbers, R. Haeb-Umbach, in: DCA Workshop, New York, USA, 2019.

Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks
J. Ebbers, L. Drude, R. Haeb-Umbach, A. Brendel, W. Kellermann, in: CAMSAP 2019, Guadeloupe, West Indies, 2019.

Privacy-preserving Variational Information Feature Extraction for Domestic Activity Monitoring Versus Speaker Identification
A. Nelus, J. Ebbers, R. Haeb-Umbach, R. Martin, in: INTERSPEECH 2019, Graz, Austria, 2019.

Performance of Mask Based Statistical Beamforming in a Smart Home Scenario
J. Heymann, M. Bacchiani, T.N. Sainath, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 6722–6726.

Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection
J. Ebbers, A. Nelus, R. Martin, R. Haeb-Umbach, in: DAGA 2018, München, 2018.

Frame-Online DNN-WPE Dereverberation
J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: IWAENC 2018, Tokio, Japan, 2018.

Smoothing along Frequency in Online Neural Network Supported Acoustic Beamforming
J. Heitkaemper, J. Heymann, R. Haeb-Umbach, in: ITG 2018, Oldenburg, Germany, 2018.

Integration neural network based beamforming and weighted prediction error dereverberation
L. Drude, C. Boeddeker, J. Heymann, K. Kinoshita, M. Delcroix, T. Nakatani, R. Haeb-Umbach, in: INTERSPEECH 2018, Hyderabad, India, 2018.

NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing
L. Drude, J. Heymann, C. Boeddeker, R. Haeb-Umbach, in: ITG 2018, Oldenburg, Germany, 2018.

Machine learning techniques for semantic analysis of dysarthric speech: An experimental study
V. Despotovic, O. Walter, R. Haeb-Umbach, Speech Communication 99 (2018) 242-251 (Elsevier B.V.) (2018).