Abstract. Artificial neural networks have become state-of-the-art in the task of language modelling on a small corpora. While feed-forward networks are able to take into account only a fixed context length to predict the next word, recurrent neural networks (RNN) can take advantage of all previous words. Due the difficulties in training of RNN, the way could be in using Long Short Term Memory (LSTM) neural network architecture. In this work, we show an application of LSTM network with extensions on a language modelling task with Czech spontaneous phone calls. Experiments show considerable improvements in perplexity and WER on recognition system over n-gram baseline.

Chromium (Cr) is considered as an important mineral, involved in biochemical reactions in human metabolic pathways. Organically bound Cr supplementation has been suggested to improve glycemia especially in patients with type 2 diabetes mellitus, but there are conflicting reports on efficacy. Effect of Cr is not clear in prediabetes status. Seventy patients with metabolic syndrome and impaired glucose tolerance (IGT), who are observed and treated in the Center of Preventive Cardiology of the University Hospital in Pilsen, were included in the prospective, randomized, double-blind, and placebo-controlled clinical study. Effect of Cr-enriched yeast (200 μg of elementary Cr in the morning and 100 μg in the evening) on glucose, lipid metabolism, fat tissue hormones, oxidative stress, and DNA damage markers was analyzed. There were no significant changes in glucose and lipid parameters, oxidative stress, or other laboratory markers. Only resting heart rate was significantly reduced in patients treated by Cr yeast, reflecting reduced sympathetic activity. This could represent an important cardiovascular risk reduction in patients with high cardiometabolic risk.

The aim of this paper is to investigate the benefit of information from a speaker change detection system based on Convolutional Neural Network (CNN) when applied to the process of accumulation of statistics for an i-vector generation. The investigation is carried out on the problem of diarization. In our system, the output of the CNN is a probability value of a speaker change in a conversation for a given time segment. According to this probability, we cut the conversation into short segments that are then represented by the i-vector (to describe a speaker in it). We propose a technique to utilize the information from the CNN for the weighting of the acoustic data in a segment to refine the statistics accumulation process. This technique enables us to represent the speaker better in the final i-vector. The experiments on the English part of the CallHome corpus show that our proposed refinement of the statistics accumulation is beneficial with the relative improvement of Diarization Error Rate almost by 16 % when compared to the speaker diarization system without statistics refinement.

Current Czech schoolchildren showed a doubled prevalence of OW and OB during the last two decades and simultaneously during nearly three decades there were more than doubled prevalence of "poor" or "under normal" MF of children, with overall dramatic decrease of MF in current schoolchildren.

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