von Neumann, T., Kinoshita, K., Boeddeker, C., Delcroix, M., &; Haeb-Umbach, R. (2023). Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 576–589.https://doi.org/10.1109/taslp.2022.3228629 
LibreCat: /record/35602
DOI: 10.1109/taslp.2022.3228629

von Neumann, T., Boeddeker, C., Delcroix, M., &; Haeb-Umbach, R. (2023). MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems. Proc. CHiME 2023 Workshop on Speech Processing in Everyday Environments. CHiME 2023 Workshop on Speech Processing in Everyday Environments, Dublin.
LibreCat: /record/48275

von Neumann, T., Kinoshita, K., Boeddeker, C., Delcroix, M., &; Haeb-Umbach, R. (2021). Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers. Interspeech 2021. Interspeech.https://doi.org/10.21437/interspeech.2021-1177 
LibreCat: /record/26770
DOI: 10.21437/interspeech.2021-1177

Heitkaemper, J., Jakobeit, D., Boeddeker, C., Drude, L., &; Haeb-Umbach, R. (2020). Demystifying TasNet: A Dissecting Approach. ICASSP 2020 Virtual Barcelona Spain.
LibreCat: /record/20504

Heitkaemper, J., Schmalenstroeer, J., &; Haeb-Umbach, R. (2020). Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments. INTERSPEECH 2020 Virtual Shanghai China.
LibreCat: /record/20505

Abramov, O., Kopp, S., Nemeth, A., Kern, F., Mertens, U., &; Rohlfing, K. (2018). Towards a Computational Model of Child Gesture-Speech Production. KOGW: Computational Approaches to Cognitive Science.
LibreCat: /record/17557

Abramov, O., Kopp, S., Nemeth, A., Kern, F., Mertens, U., &; Rohlfing, K. (2018). Towards a Computational Model of Child Gesture-Speech Production. KOGW: Computational Approaches to Cognitive Science.
LibreCat: /record/17179

Chinaev, A., &; Haeb-Umbach, R. (2015). On Optimal Smoothing in Minimum Statistics Based Noise Tracking. In Interspeech 2015 (pp. 1785–1789).
LibreCat: /record/11739

Heymann, J., Haeb-Umbach, R., Golik, P., &; Schlueter, R. (2015). 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 (pp. 5053–5057).https://doi.org/10.1109/ICASSP.2015.7178933 
LibreCat: /record/11813
DOI: 10.1109/ICASSP.2015.7178933

Drude, L., Chinaev, A., Tran Vu, D. H., &; Haeb-Umbach, R. (2014). 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) (pp. 213–217).
LibreCat: /record/11753

Leutnant, V., Krueger, A., &; Haeb-Umbach, R. (2014). 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, 22(1), 95–109.https://doi.org/10.1109/TASLP.2013.2285480 
LibreCat: /record/11861
DOI: 10.1109/TASLP.2013.2285480

Li, J., Deng, L., Gong, Y., &; Haeb-Umbach, R. (2014). An Overview of Noise-Robust Automatic Speech Recognition. IEEE Transactions on Audio, Speech and Language Processing, 22(4), 745–777.https://doi.org/10.1109/TASLP.2014.2304637 
LibreCat: /record/11867
DOI: 10.1109/TASLP.2014.2304637

Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., &; Haeb-Umbach, R. (2013). GMM-based significance decoding. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 6827–6831).https://doi.org/10.1109/ICASSP.2013.6638984 
LibreCat: /record/11716
DOI: 10.1109/ICASSP.2013.6638984

Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Habets, E., Haeb-Umbach, R.,  Raj, B. (2013). 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  (pp. 22–23).
LibreCat: /record/11841

Leutnant, V., Krueger, A., &; Haeb-Umbach, R. (2013). Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing, 21(8), 1640–1652.https://doi.org/10.1109/TASL.2013.2258013 
LibreCat: /record/11862
DOI: 10.1109/TASL.2013.2258013

Vu, D. H. T., &; Haeb-Umbach, R. (2013). 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) (pp. 863–867).https://doi.org/10.1109/ICASSP.2013.6637771 
LibreCat: /record/11917
DOI: 10.1109/ICASSP.2013.6637771

Chinaev, A., Krueger, A., Tran Vu, D. H., &; Haeb-Umbach, R. (2012). Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012).
LibreCat: /record/11745

Leutnant, V., Krueger, A., &; Haeb-Umbach, R. (2012). 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.
LibreCat: /record/11864

Krueger, A., Warsitz, E., &; Haeb-Umbach, R. (2011). Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation. IEEE Transactions on Audio, Speech, and Language Processing, 19(1), 206–219.https://doi.org/10.1109/TASL.2010.2047324 
LibreCat: /record/11850
DOI: 10.1109/TASL.2010.2047324

Fischer, K., Foth, K., Rohlfing, K., &; Wrede, B. (2011). Mindful tutors: Linguistic choice and action demonstration in speech to infants and a simulated robot. Interaction Studies, 12(1), 134–161.https://doi.org/10.1075/is.12.1.06fis 
LibreCat: /record/17233
DOI: 10.1075/is.12.1.06fis