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8 Publications

2017 | Journal Article | LibreCat-ID: 5633
G. Schryen et al., “Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?,” Communications of the AIS, 2017.
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2017 | Conference Paper | LibreCat-ID: 10676
N. Ho, P. Kaufmann, and M. Platzner, “Evolvable caches: Optimization of reconfigurable cache mappings for a LEON3/Linux-based multi-core processor,” in 2017 International Conference on Field Programmable Technology (ICFPT), 2017, pp. 215–218.
LibreCat | DOI

2015 | Conference Paper | LibreCat-ID: 11813
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.
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2014 | Journal Article | LibreCat-ID: 11867
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.
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2013 | Conference Paper | LibreCat-ID: 11816
M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning,” in 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.
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2009 | Journal Article | LibreCat-ID: 11938
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.
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2006 | Conference Paper | LibreCat-ID: 11943
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.
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2001 | Journal Article | LibreCat-ID: 11778
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.
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