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Development of an English public transport information dialogue system
Vejman, Martin ; Jurčíček, Filip (advisor) ; Peterek, Nino (referee)
This thesis presents a development of an English spoken dialogue system based on the Alex dialogue system framework. The work describes a component adaptation of the framework for a different domain and language. The system provides public transport information in New York. This work involves creating a statistical model and the deployment of custom Kaldi speech recognizer. Its performance was better in comparison with the Google Speech API. The comparison was based on a subjective user satisfaction acquired by crowdsourcing. Powered by TCPDF (www.tcpdf.org)
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Development of speech enabled web games using CloudASR
Milota, Jan ; Jurčíček, Filip (advisor) ; Vidová Hladká, Barbora (referee)
The main goal of this thesis is to design and implement a piece of software for playful language learning, using web technologies and the fresh CloudASR library. A common user interacts with their web browser almost exclusively using a mouse and keyboard. Thanks to the software this thesis represents the user has an opportunity to delve into sometimes unpopular language learning process using his natural voice. This fact presents new and exciting possibilities, mainly regarding user interactivity. A lot of stress has been put to user friendliness, graphical fidelity and to the competitive aspect of language education, exploiting Facebook integration and point-scoring leader boards. Powered by TCPDF (www.tcpdf.org)
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Approximative Bayes methods for belief monitoring in spoken dialogue systems
Marek, David ; Jurčíček, Filip (advisor) ; Žabokrtský, Zdeněk (referee)
The most important component of virtually any dialog system is a dialogue manager. The aim of the dialog manager is to propose an action (a continuation of the dialogue) given the last dialog state. The dialog state summarises all the past user input and the system input and ideally it includes all information necessary for natural progress in the dialog. For the dialog manager to work efficiently, it is important to model the probability distribution over all dialog states as precisely as possible. It is possible that the set of dialog states will be very large, so approximative methods usually must be used. In this thesis we will discuss an implementation of approximate Bayes methods for belief state monitoring. The result is a library for dialog state monitoring in real dialog systems. 1
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Unsupervised Dependency Parsing
Mareček, David ; Žabokrtský, Zdeněk (advisor) ; Jurčíček, Filip (referee) ; Sogaard, Anders (referee)
Unsupervised dependency parsing is an alternative approach to identifying relations between words in a sentence. It does not require any annotated treebank, it is independent of language theory and universal across languages. However, its main disadvantage is its so far quite low parsing quality. This thesis discusses some previous works and introduces a novel approach to unsupervised parsing. Our dependency model consists of four submodels: (i) edge model, which controls the distribution of governor-dependent pairs, (ii) fertility model, which controls the number of node's dependents, (iii) distance model, which controls the length of the dependency edges, and (iv) reducibility model. The reducibility model is based on a hypothesis that words that can be removed from a sentence without violating its grammaticality are leaves in the dependency tree. Induction of the dependency structures is done using Gibbs sampling method. We introduce a sampling algorithm that keeps the dependency trees projective, which is a very valuable constraint. In our experiments across 30 languages, we discuss the results of various settings of our models. Our method outperforms the previously reported results on a majority of the test languages.
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