M
Mirko Hannemann
Researcher at Brno University of Technology
Publications - 20
Citations - 6525
Mirko Hannemann is an academic researcher from Brno University of Technology. The author has contributed to research in topics: Bayesian probability & Vocabulary. The author has an hindex of 12, co-authored 19 publications receiving 5818 citations. Previous affiliations of Mirko Hannemann include Microsoft & Otto-von-Guericke University Magdeburg.
Papers
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Proceedings Article
The Kaldi Speech Recognition Toolkit
Daniel Povey,Arnab Ghoshal,Gilles Boulianne,Lukas Burget,Ondrej Glembek,Nagendra Kumar Goel,Mirko Hannemann,Petr Motlicek,Yanmin Qian,Petr Schwarz,Jan Silovsky,Georg Stemmer,Karel Vesely +12 more
TL;DR: The design of Kaldi is described, a free, open-source toolkit for speech recognition research that provides a speech recognition system based on finite-state automata together with detailed documentation and a comprehensive set of scripts for building complete recognition systems.
Proceedings ArticleDOI
Semi-supervised training of Deep Neural Networks
TL;DR: It is beneficial to reduce the disproportion in amounts of transcribed and untranscribed data by including the transcribed data several times, as well as to do a frame-selection based on per-frame confidences derived from confusion in a lattice.
Proceedings ArticleDOI
Generating exact lattices in the WFST framework
Daniel Povey,Mirko Hannemann,Gilles Boulianne,Lukas Burget,Arnab Ghoshal,Milos Janda,Martin Karafiat,Stefan Kombrink,Petr Motlicek,Yanmin Qian,Korbinian Riedhammer,Karel Vesely,Ngoc Thang Vu +12 more
TL;DR: A lattice generation method that is exact, i.e. it satisfies all the natural properties the authors would want from a lattice of alternative transcriptions of an utterance, and does not introduce substantial overhead above one-best decoding.
Proceedings ArticleDOI
Score normalization and system combination for improved keyword spotting
Damianos Karakos,Richard Schwartz,Stavros Tsakalidis,Le Zhang,Shivesh Ranjan,Tim Ng,Roger Hsiao,Guruprasad Saikumar,Ivan Bulyko,Long Nguyen,John Makhoul,Frantisek Grezl,Mirko Hannemann,Martin Karafiat,Igor Szöke,Karel Vesely,Lori Lamel,Viet Bac Le +17 more
TL;DR: Two techniques are shown to yield improved Keyword Spotting (KWS) performance when using the ATWV/MTWV performance measures, which resulted in the highest performance for the official surprise language evaluation for the IARPA-funded Babel project in April 2013.
Proceedings ArticleDOI
Combination of strongly and weakly constrained recognizers for reliable detection of OOVS
Lukas Burget,Petr Schwarz,Pavel Matejka,Mirko Hannemann,Ariya Rastrow,Christopher White,Sanjeev Khudanpur,Hynek Hermansky,Jan Cernocky +8 more
TL;DR: Substantial improvement is obtained when posteriors from two systems - strongly constrained (LVCSR) and weakly constrained (phone posterior estimator) are combined and it is shown that this approach is also suitable for detection of general recognition errors.