P
Petr Schwarz
Researcher at Brno University of Technology
Publications - 42
Citations - 8385
Petr Schwarz is an academic researcher from Brno University of Technology. The author has contributed to research in topics: NIST & Speaker recognition. The author has an hindex of 22, co-authored 42 publications receiving 7632 citations. Previous affiliations of Petr Schwarz include Oregon Health & Science University.
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.
Journal ArticleDOI
The subspace Gaussian mixture model-A structured model for speech recognition
Daniel Povey,Lukas Burget,Mohit Agarwal,Pinar Akyazi,Feng Kai,Arnab Ghoshal,Ondřej Glembek,Nagendra Kumar Goel,Martin Karafiat,Ariya Rastrow,Richard Rose,Petr Schwarz,Samuel Thomas +12 more
TL;DR: A new approach to speech recognition, in which all Hidden Markov Model states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state, appears to give better results than a conventional model.
Journal ArticleDOI
Fusion of Heterogeneous Speaker Recognition Systems in the STBU Submission for the NIST Speaker Recognition Evaluation 2006
Niko Brümmer,Lukas Burget,Jan Cernocky,Ondrej Glembek,Frantisek Grezl,Martin Karafiat,D.A. van Leeuwen,Pavel Matejka,Petr Schwarz,Albert Strasheim +9 more
TL;DR: The STBU speaker recognition system was a combination of three main kinds of subsystems, which performed well in the NIST Speaker Recognition Evaluation 2006 (SRE).
Proceedings ArticleDOI
Hierarchical Structures of Neural Networks for Phoneme Recognition
TL;DR: This paper deals with phoneme recognition based on neural networks (NN), and focuses on temporal patterns (TRAPs) and novel split temporal context (STC) phoneme recognizers and investigates into tandem NN architectures.
Proceedings ArticleDOI
Subspace Gaussian Mixture Models for speech recognition
Daniel Povey,Lukas Burget,Mohit Agarwal,Pinar Akyazi,Kai Feng,Arnab Ghoshal,Ondrej Glembek,Nagendra Kumar Goel,Martin Karafiat,Ariya Rastrow,Richard Rose,Petr Schwarz,Samuel Thomas +12 more
TL;DR: An acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space, and this style of acoustic model allows for a much more compact representation.