Brno University of Technology

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.

A new modality for quantitative evaluation of Parkinson's disease: In-air movement

Abstract: Parkinsons disease (PD) is neurodegenerative disorder with very high prevalence rate occurring mainly among elderly. One of the most typical symptoms of PD is deterioration of handwriting that is usually the first manifestation of Parkinsons disease. In this study, a new modality - in-air trajectory during handwriting - is proposed to efficiently diagnose PD. Experimental results showed that analysis of in-air trajectories is capable of assessing subtle motor abnormalities that are connected with PD. Moreover, conjunction of in-air trajectories with conventional on-surface handwriting allows us to build predictive model with PD classification accuracy over 80%. In total, we compute over 600 handwriting features. Then, we select smaller subset of these features using two feature selection algorithms: Mann-Whitney U-test filter and relief algorithm, and map these feature subsets to binary classification response using support vector machines.

Phonotactic language recognition using i-vectors and phoneme posteriogram counts

Abstract: This paper describes a novel approach to phonotactic LID, where instead of using soft-counts based on phoneme lattices, we use posteriogram to obtain n-gram counts. The high-dimensional vectors of counts are reduced to low-dimensional units for which we adapted the commonly used term i-vectors. The reduction is based on multinomial subspace modeling and is designed to work in the total-variability space. The proposed technique was tested on the NIST 2009 LRE set with better results to a system based on using soft-counts (Cavg on 30s: 3.15% vs 3.43%), and with very good results when fused with an acoustic i-vector LID system (Cavg on 30s acoustic 2.4% vs 1.25%). The proposed technique is also compared with another low dimensional projection system based on PCA. In comparison with the original soft-counts, the proposed technique provides better results, reduces the problems due to sparse counts, and avoids the process of using pruning techniques when creating the lattices.

Unsteadiness in effervescent sprays: a new evaluation method and the influence of operational conditions

Abstract: A new evaluation method of unsteadiness of the spray generated by an effervescent atomizer is presented. The method is based on measurements of pressure fluctuations in the atomizer mixing chamber. Measurements, made under different atomizer operational conditions, show the spray unsteadiness depends mainly on Gas-to-Liquid-Ratio (GLR). Decrease in GLR causes the spray to become more unsteady. The relation between atomizer internal two-phase flow pattern and the spray unsteadiness is elucidated by visualization of the internal twophase flow using a digital camera and the use of published two-phase flow maps. Findings of the new method are complemented and confronted with the results obtained by the use of spray unsteadiness evaluation method of Edwars & Marx.

BUT BABEL system for spontaneous Cantonese

Abstract: This paper presents our work on speech recognition of Cantonese spontaneous telephone conversations. The key-points include feature extraction by 6-layer Stacked Bottle-Neck neural network and using fundamental frequency information at its input. We have also investigated into robustness of SBN training (silence, normalization) and shown an efficient combination with PLP using Region-Dependent transforms. A combination of RDT with another popular adaptation technique (SAT) was shown beneficial. The results are reported on BABEL Cantonese data. Index Terms: speech recognition, discriminative training, bottle-neck neural networks, region-dependent transforms