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Institution

Brno University of Technology

EducationBrno, Czechia
About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Fracture mechanics & Filter (video). The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.


Papers
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Book ChapterDOI
12 Sep 2005
TL;DR: This paper describes several ways of acoustic keywords spotting (KWS), based on Gaussian mixture model (GMM) hidden Markov models (HMM) and phoneme posterior probabilities from FeatureNet, and concludes that phoneme posteriors outperform conventional GMM/HMM system.
Abstract: This paper describes several ways of acoustic keywords spotting (KWS), based on Gaussian mixture model (GMM) hidden Markov models (HMM) and phoneme posterior probabilities from FeatureNet. Context-independent and dependent phoneme models are used in the GMM/HMM system. The systems were trained and evaluated on informal continuous speech. We used different complexities of KWS recognition network and different types of phoneme models. We study the impact of these parameters on the accuracy and computational complexity, an conclude that phoneme posteriors outperform conventional GMM/HMM system.

86 citations

Proceedings ArticleDOI
06 Sep 2009
TL;DR: This work investigates into recently proposed Bottle-Neck features for ASR by imported into Split Context architecture gaining significant WER reduction and Universal Context architecture was developed which simplified the system by using only one universal ANN for all temporal splits.
Abstract: This work investigates into recently proposed Bottle-Neck features for ASR. The bottle-neck ANN structure is imported into Split Context architecture gaining significant WER reduction. Further, Universal Context architecture was developed which simplifies the system by using only one universal ANN for all temporal splits. Significant WER reduction can be obtained by applying fMPE on top of our BN features as a technique for discriminative feature extraction and further gain is also obtained by retraining model parameters using MPE criterion. The results are reported on meeting data from RT07 evaluation.

86 citations

Journal ArticleDOI
TL;DR: In this article, a methodology for SPICE modeling of a memcapacitor is described, where the model is compounded of the submodel of the state-space system, representing the memory effect of the device, which depends on the concrete physical implementation of a device, and of a capacitor, the capacitance of which is controlled by the system state.
Abstract: A methodology for SPICE modelling of a memcapacitor is described. The model is compounded of the submodel of the state-space system, representing the memory effect of the device, which depends on the concrete physical implementation of the device, and of a capacitor, the capacitance of which is controlled by the system state. The basic fingerprints of the memcapacitor are demonstrated in the example of a charge-controlled memcapacitor.

86 citations

Journal ArticleDOI
TL;DR: In this paper, the 3D surface morphology of aluminum nitride (AlN) epilayers on sapphire substrates prepared by magnetron sputtering was studied by combination of atomic force microscopy (AFM) and fractal analysis methods.

86 citations

Journal ArticleDOI
TL;DR: This work explores molecular, morphological as well as biotechnological features of the highly promising polyhydroxyalkanoates (PHA) producer Halomonas halophila and its potential for industrial PHB production.

85 citations


Authors

Showing all 6383 results

NameH-indexPapersCitations
Georg Kresse111430244729
Patrik Schmuki10976352669
Michael Schmid8871530874
Robert M. Malina8869138277
Jiří Jaromír Klemeš6456514892
Alessandro Piccolo6228414332
René Kizek6167216554
George Danezis5920911516
Stevo Stević583749832
Edvin Lundgren5728610158
Franz Halberg5575015400
Vojtech Adam5561114442
Lukas Burget5325221375
Jan Cermak532389563
Hynek Hermansky5131714372
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202328
2022106
20211,053
20201,010
20191,214
20181,131