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Pronunciation Features of Czech Language - Dialect Analysis
Michlíková, Vendula ; Peterek, Nino (advisor) ; Korvas, Matěj (referee)
We implemented Výrče:SW, a tool for collecting and analysing audio recordings without the necessity of supervisor's assistance. The tool allows creating a wide range of recording scenarios, including the possibility to analyse the recordings and show the results. Using the created tool, we collected Výrče:Korpus, a read audio corpus of 34 speakers and 2376 utterances of 7 hours in length. The corpus also includes questionnaires that provide information about the dialect reliability of speakers. Sufficient amounts of speakers for dialect analysis are from the Central Bohemian dialect area and Silesian dialect area. On the two selected groups, we trained a simple monophone dialect recogniser based on Hidden Markov Models. Powered by TCPDF (www.tcpdf.org)
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User simulation for statistical dialogue systems
Michlíková, Vendula ; Jurčíček, Filip (advisor) ; Žabokrtský, Zdeněk (referee)
The purpose of this thesis is to develop and evaluate user simulators for a spoken dialogue system. Created simulators are operating on dialogue act level. We implemented a bigram simulator as a baseline system. Based on the baseline simulator, we created another bigram simulator that is trained on dialogue acts without slot values. The third implemented simulator is similar to an implemen- tation of a dialogue manager. It tracks its dialogue state and learns a dialogue strategy based on the state using supervised learning. The user simulators are implemented in Python 2.7, in ALEX framework for dialogue system development. Simulators are developed for PTICS application which operates in the domain of public transport information. Simulators are trained and evaluated using real human-machine dialogues collected with PTICS application. 1
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Pronunciation Features of Czech Language - Dialect Analysis
Michlíková, Vendula ; Peterek, Nino (advisor) ; Korvas, Matěj (referee)
We implemented Výrče:SW, a tool for collecting and analysing audio recordings without the necessity of supervisor's assistance. The tool allows creating a wide range of recording scenarios, including the possibility to analyse the recordings and show the results. Using the created tool, we collected Výrče:Korpus, a read audio corpus of 34 speakers and 2376 utterances of 7 hours in length. The corpus also includes questionnaires that provide information about the dialect reliability of speakers. Sufficient amounts of speakers for dialect analysis are from the Central Bohemian dialect area and Silesian dialect area. On the two selected groups, we trained a simple monophone dialect recogniser based on Hidden Markov Models. Powered by TCPDF (www.tcpdf.org)
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