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Controlling Mobile App by Voice
Cologna, Adam ; Szőke, Igor (referee) ; Herout, Adam (advisor)
The aim of this bachelor's thesis is to explore, analyze, and compare available libraries for controlling mobile applications with voice commands. According to the thesis requirements, I solved the problem primarily for the Android operating system. I have considered not only libraries for keyword detection but also those using the speech recognition technology. For the selected libraries, I conducted accuracy testing for voice command detection and performance testing. To facilitate the integration of the chosen libraries, an application was developed in the modern programming language Kotlin using Jetpack Compose. The main contribution of this work is the experiments carried out and the resulting recommendations regarding the examined libraries. Among the most effective solutions were those from Microsoft Azure and the Android Speech Recognizer class. The main obstacles for each library were the distance between the speaker and the microphone, as well as distracting background music.
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Human-machine collaboration - using speech processing
Kisler, Štěpán ; Hůlka, Tomáš (referee) ; Juříček, Martin (advisor)
This bachelor's thesis focuses on the design and implementation of a voice control system for the UR3 CB series collaborative robot from Universal Robots, aiming to simplify human-robot interaction. The introduction provides an overview of collaborative robotics, including its history, successful applications, and the possibilities of programming collaborative robots. Additionally, it explores speech recognition technology, covering its applications, history, and methods. The practical section compares existing speech recognition systems and selects the most suitable one for robot voice control. It also details the development of a voice control program in Python and the testing of the entire system, both in simulation and real-world conditions in a robotics laboratory.
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Implementation of Simple Speech Recognizer in a Web Browser
Crkoň, Jakub ; Glembek, Ondřej (referee) ; Szőke, Igor (advisor)
The goal of this project is to implement simple speech recognizer for web browser. This paper describes fundamental components required for implementing speech recognizer and techniques which are used for optimization process of speech recognition in web browser. At first, the paper focuses on introduction of speech recognition theory. It describes individual parts and principles of speech recognizer. In next section, thesis describes design, implementation and principles of acceleration of speech recognizer with limited computing resources of web browser. The implementation is divided into modules making up the library for usage in web browser. The library is easily extendable and usable in various web applications. Finally, it discusses potential directions of development and usability of this project.
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Speech Technology Application in Pronunciation Training and Foreign Language Learning
Barotová, Štěpánka ; Žmolíková, Kateřina (referee) ; Szőke, Igor (advisor)
Tato diplomová práce pojednává o využití algoritmu Dynamic Time Warping (DTW) pro automatické hodnocení výslovnosti anglického jazyka. Práce se zaměřuje na vylepšení již existující aplikace pro výuku výslovnosti, a to ve třech oblastech: uživatelské rozhraní, samotný algoritmus a korektivní zpětná vazba uživateli. První část se věnuje přehledu technik používaných v této oblasti, následně je představen nový design uživatelského rozhraní, popsán navržený systém a experimenty. Experimenty se zaměřují na problematiku detekce chyb na úrovni fonémů, na detekci chyb v primárním důrazu na úrovni slabik a na hodnocení intonace na úrovni slov. Všechny použité metody jsou navrženy tak, aby poskytovaly korektivní zpětnou vazbu uživateli. V poslední části je popsáno, jak byly všechny tři vylepšené oblasti aplikace otestovány.
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Recognition of Isolated Words for Electronic Dictionaries
Hrdlička, Pavel ; Szőke, Igor (referee) ; Grézl, František (advisor)
This work is concerned with creation of isolated word recognizer for electronic dictionaires, testing its functionality on data sample and improvement by normalisation and speaker adaptation techniques. Word recognizer is built on HTK (Hidden Markov Model Toolkit). At the beginning of this document, the main aims of the work are set. In the next chapter is theoretical analysis, which describes process of recognition of isolated words with hidden Markov models. Next chapter specifies the speech data, which were used for testing. Other resources for building recognizer, like models, dictionary and grammar are described in next chapter. Before creation of recognizer, it was necessary to solve conversion between the phonemes set which was used in dictionary and set, which uses the recognizer. The recognizer was built with 8~kHz models first, than 16~kHz models were also used. Normalisation and speaker adaptation techniques were used. Obtained data were processed and results are analyzed in separate chapter. Finally is discussed, if the goals of the work were reached and what are the next steps of application development.
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Voice Recording and Search for Skype
Nytra, Jiří ; Szőke, Igor (referee) ; Schwarz, Petr (advisor)
This work deals with the creation of a program communicating with Skype, which provides record calls in which can search for keywords by using advanced speech recognition technology. The work is presented and the interface protocol to communicate with Skype, call recording and method LVCSR for searching keywords.
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Penetration Tests of Speaker Verification System
Wojnar, Filip ; Landini, Federico Nicolás (referee) ; Plchot, Oldřich (advisor)
Cílem práce je provést penetrační testy na systému pro automatickou verifikace řečníka za použití syntézy řeči. Práce se zabývá fungování systému pro automatickou verifikaci řečníka a spoofing útoky na systémy, zabývající se touto problematikou. Práce se také podrobnějí zabývá fungováním syntézy řeči. Pozdější kapitoly se zabývají realizací penetračních testů a výsledky, které nám tyto testy přinesly.
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Parallel Training of Neural Networks for Speech Recognition
Veselý, Karel ; Fousek, Petr (referee) ; Burget, Lukáš (advisor)
This thesis deals with different parallelizations of training procedure for artificial neural networks. The networks are trained as phoneme-state acoustic descriptors for speech recognition. Two effective parallelization strategies were implemented and compared. The first strategy is data parallelization, where the training is split into several POSIX threads. The second strategy is node parallelization, which uses CUDA framework for general purpose computing on modern graphic cards. The first strategy showed a 4x speed-up, while using the second strategy we observed nearly 10x speed-up. The Stochastic Gradient Descent algorithm with error backpropagation was used for the training. After a short introduction, the second chapter of this thesis shows the motivation and introduces the neural networks into the context of speech recognition. The third chapter is theoretical, the anatomy of a neural network and the used training method are discussed. The following chapters are focused on the design and implementation of the project, while the phases of the iterative development are described. The last extensive chapter describes the setup of the testing system and reports the experimental results. Finally, the obtained results are concluded and the possible extensions of the project are proposed.
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Unsupervised Adaptation of Speech Recognizer
Švec, Ján ; Karafiát, Martin (referee) ; Schwarz, Petr (advisor)
The goal of this thesis is to design and test techniques for unsupervised adaptation of speech recognizers on some audio data without any textual transcripts. A training set is prepared at first, and a baseline speech recognition system is trained. This sistem is used to transcribe some unseen data. We will experiment with an adaptation data selection process based on some speech transcript quality measurement. The system is re-trained on this new set than, and the accuracy is evaluated. Then we experiment with the amount of adaptation data.
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