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Metodický postup nácviku úderů do makiwary
Hofman, Michal ; Venzara, Jan (vedoucí práce) ; Štěpánek, Jan (oponent)
Název práce: Metodický postup nácviku úderů do makiwary Cíl práce: Cílem této diplomové práce je vytvoření metodického postupu nácviku úderů do makiwary. Podrobný popis a konstrukce této tréninkové pomůcky a možnosti jejího použití. Dále seznámit s anatomií úderových ploch seiken, jakožto i jejich funkčních a anatomických změn, ke kterým dochází při užívání makiwary v tréninkovém procesu. Použité metody: K vytvoření metodického postupu nácviku úderů do makiwary jsem uplatnil techniky založené na přímém a nepřímém pozorování a především metody odborného posuzování - ratingu. Dále metody interviw a sběrů dat. Výsledky: Výsledkem diplomové práce je vytvoření metodického postupu nácviku úderů do makiwary, obecných pravidel, fázemi tréninkového procesu a třech variant provádění úderu gjaku cuki. Dále seznámení s návodem výroby a montáže různých druhů makiwar. Dále popsání anatomických a funkčních změn na úderových plochách seiken, ke kterým dochází při tréninkovém procesu na makiwaře. Klíčová slova: Makiwara, úderové plochy, seiken, tréninkový proces, gjaku cuki.
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Efektivní komunikace a vyjednávání
Fábryová, Dana ; Matějka, Zdeněk (vedoucí práce) ; Peterková, Jana (oponent)
Tato bakalářská práce se zabývá uměním komunikace a jeho důležitostí nejen na poli mezinárodních vztahů. Popisuje zde nejdůležitější zásady efektivního jednání, je zde kladen důraz nejen na obecná pravidla prvního či následného kontaktu, ale zejména na rozpoznání člověka, se kterým jednáme, a výběr vhodné techniky pro vyjednávání. Práce využívá typologie DISC, kterou obohacuje konkrétními otázkami, které jsou na jednotlivé typologické profily účinné. Bakalářská práce je pojata velice prakticky a vychází nejen z oborné literatury, nýbrž i z vlasních průzkumů a několikaletých pracovních zkušeností.
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Subspace Modeling of Prosodic Features for Speaker Verification
Kockmann, Marcel ; Kenny, Patrick (oponent) ; Nöth, Elmar (oponent) ; Černocký, Jan (vedoucí práce)
The thesis investigates into speaker verification by means of prosodic features. This includes an appropriate representation of speech by measurements of pitch, energy and duration of speech sounds. Two diverse parameterization methods are investigated: the first leads to a low-dimensional well-defined set, the second to a large-scale set of heterogeneous prosodic features. The first part of this work concentrates on the development of so called prosodic contour features. Different modeling techniques are developed and investigated, with a special focus on subspace modeling. The second part focuses on a novel subspace modeling technique for the heterogeneous large-scale prosodic features. The model is theoretically derived and experimentally evaluated on official NIST Speaker Recognition Evaluation tasks. Huge improvements over the current state-of-the-art in prosodic speaker verification were obtained. Eventually, a novel fusion method is presented to elegantly combine the two diverse prosodic systems. This technique can also be used to fuse the higher-level systems with a high-performing cepstral system, leading to further significant improvements.
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STATISTICAL LANGUAGE MODELS BASED ON NEURAL NETWORKS
Mikolov, Tomáš ; Zweig, Geoffrey (oponent) ; Hajič,, Jan (oponent) ; Černocký, Jan (vedoucí práce)
Statistical language models are crucial part of many successful applications, such as automatic speech recognition and statistical machine translation (for example well-known Google Translate). Traditional techniques for estimating these models are based on Ngram counts. Despite known weaknesses of N-grams and huge efforts of research communities across many fields (speech recognition, machine translation, neuroscience, artificial intelligence, natural language processing, data compression, psychology etc.), N-grams remained basically the state-of-the-art. The goal of this thesis is to present various architectures of language models that are based on artificial neural networks. Although these models are computationally more expensive than N-gram models, with the presented techniques it is possible to apply them to state-of-the-art systems efficiently. Achieved reductions of word error rate of speech recognition systems are up to 20%, against stateof-the-art N-gram model. The presented recurrent neural network based model achieves the best published performance on well-known Penn Treebank setup.
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Optimization of Gaussian Mixture Subspace Models and Related Scoring Algorithms in Speaker Verification
Glembek, Ondřej ; Brummer, Niko (oponent) ; Campbell,, William (oponent) ; Burget, Lukáš (vedoucí práce)
This thesis deals with Gaussian Mixture Subspace Modeling in automatic speaker recognition. The thesis consists of three parts. In the first part, Joint Factor Analysis (JFA) scoring methods are studied. The methods differ mainly in how they deal with the channel of the tested utterance. The general JFA likelihood function is investigated and the methods are compared both in terms of accuracy and speed. It was found that linear approximation of the log-likelihood function gives comparable results to the full log-likelihood evaluation while simplyfing the formula and dramatically reducing the computation speed. In the second part, i-vector extraction is studied and two simplification methods are proposed. The motivation for this part was to allow for using the state-of-the-art technique on small scale devices and to setup a simple discriminative-training system. It is shown that, for long utterances, while sacrificing the accuracy, we can get very fast and compact i-vector systems. On a short-utterance(5-second) task, the results of the simplified systems are comparable to the full i-vector extraction. The third part deals with discriminative training in automatic speaker recognition. Previous work in the field is summarized and---based on the knowledge from the earlier chapters of this work---discriminative training of the i-vector extractor parameters is proposed. It is shown that discriminative re-training of the i-vector extractor can improve the system if the initial estimation is computed using the generative approach.
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Relational Verification of Programs with Integer Data
Konečný, Filip ; Bouajjani, Ahmed (oponent) ; Jančar, Petr (oponent) ; Vojnar, Tomáš (vedoucí práce)
This work presents novel methods for verification of reachability and termination properties of programs that manipulate unbounded integer data. Most of these methods are based on acceleration techniques which compute transitive closures of program loops. We first present an algorithm that accelerates several classes of integer relations and show that the new method performs up to four orders of magnitude better than the previous ones. On the theoretical side, our framework provides a common solution to the acceleration problem by proving that the considered classes of relations are periodic. Subsequently, we introduce a semi-algorithmic reachability analysis technique that tracks relations between variables of integer programs and applies the proposed acceleration algorithm to compute summaries of procedures in a modular way. Next, we present an alternative approach to reachability analysis that integrates predicate abstraction with our acceleration techniques to increase the likelihood of convergence of the algorithm. We evaluate these algorithms and show that they can handle a number of complex integer programs where previous approaches failed. Finally, we study the termination problem for several classes of program loops and show that it is decidable. Moreover, for some of these classes, we design a polynomial time algorithm that computes the exact set of program configurations from which nonterminating runs exist. We further integrate this algorithm into a semi-algorithmic method that analyzes termination of integer programs, and show that the resulting technique can verify termination properties of several non-trivial integer programs.
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HUMAN ACTION RECOGNITION IN VIDEO
Řezníček, Ivo ; Baláž, Teodor (oponent) ; Sojka, Eduard (oponent) ; Zemčík, Pavel (vedoucí práce)
This thesis focuses on the improvement of human action recognition systems. It reviews the state-of-the-art in the field of action recognition from video. It describes techniques of digital image and video capture, and explains computer representations of image and video. This thesis further describes how local feature vectors and local space-time feature vectors are used, and how captured data is prepared for further analysis, such as classification methods. This is typically done with video segments of arbitrarily varying length. The key contribution of this work explores the hypothesis that the analysis of different types of actions requires different segment lenghts to achieve optimal quality of recognition. An algorithm to find these optimal lengths is proposed, implemented, and tested. Using this algorithm, the hypothesis was experimentally proven. It was also shown that by finding the optimal length, the prediction and classification power of current algorithms is improved upon. Supporting experiments, results, and proposed exploitations of these findings are presented.
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Kompresní techniky statického obrazu
Jirounek, Matěj ; Šmirg, Ondřej (oponent) ; Krajsa, Ondřej (vedoucí práce)
Bakalářská práce pojednává o dnes používaných kompresních technikách statického obrazu, o jejich základních principech, výhodách a nevýhodách v oblasti použití a jejich vzájemném porovnání. Práce je rozdělena do sedmi kapitol, z nichž druhá pojednává rozdělení komprese dat, třetí o bezztrátové kompresi a jejich hlavními představiteli a čtvrtá naopak o ztrátové kompresi obrazu s jejími výhodami a nedostatky a také o barevných modelech. V páté kapitole jsou shrnuty použitá kritéria pro hodnocení obrazu a v šesté kapitole je ukázána implementace programu v prostředí MATLAB.
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