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Recurrent Neural Networks for Speech Recognition
Nováčik, Tomáš ; Karafiát, Martin (referee) ; Veselý, Karel (advisor)
This master thesis deals with the implementation of various types of recurrent neural networks via programming language lua using torch library. It focuses on finding optimal strategy for training recurrent neural networks and also tries to minimize the duration of the training. Furthermore various types of regularization techniques are investigated and implemented into the recurrent neural network architecture. Implemented recurrent neural networks are compared on the speech recognition task using AMI dataset, where they model the acustic information. Their performance is also compared to standard feedforward neural network. Best results are achieved using BLSTM architecture. The recurrent neural network are also trained via CTC objective function on the TIMIT dataset. Best result is again achieved using BLSTM architecture.
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Image Captioning with Recurrent Neural Networks
Kvita, Jakub ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
Tato práce se zabývá automatickým generovaním popisů obrázků s využitím několika druhů neuronových sítí. Práce je založena na článcích z MS COCO Captioning Challenge 2015 a znakových jazykových modelech, popularizovaných A. Karpathym. Navržený model je kombinací konvoluční a rekurentní neuronové sítě s architekturou kodér--dekodér. Vektor reprezentující zakódovaný obrázek je předáván jazykovému modelu jako hodnoty paměti LSTM vrstev v síti. Práce zkoumá, na jaké úrovni je model s takto jednoduchou architekturou schopen popisovat obrázky a jak si stojí v porovnání s ostatními současnými modely. Jedním ze závěrů práce je, že navržená architektura není dostatečná pro jakýkoli popis obrázků.
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Cultural legacy of Jan Palach's act and it's didactic processing
Dušková, Daniela ; Komberec, Filip (advisor) ; Neumann, Lukáš (referee)
The thesis deals with the cultural legacy of Jan Palach in song and poetry texts of the 20th and 21st century. The author of the thesis aims to present the use of these texts in teaching, which deals with (historical) topics that provoke intense and often contradictory emotional reactions - here, demonstrative suicide for the awakening of society. In the first part, she draws a brief insight into the life of Jan Palach and the events that preceded his act, and describes Palach's self-immolation itself as a demonstrative act. It also includes a philosophical and theological perspective on Palach's sacrifice. For the purposes of the lesson, the writer interprets the collected texts, thus contributing to the limited amount of scholarly Czech literature on the depiction of the act in song and poetry. In the second part of the thesis, she then uses these texts in the teaching unit design for use in history, Czech language, civics or music classes. Together with the lesson, she presents further uses of the collected material. The paper shows theresults of a pilot lesson in the seventhyear of an eight- year grammar school, including comments from the participating pupils. The work is dedicated to honouring the legacy of the 55th anniversary of the burning of Jan Palach. KEYWORDS Jan Palach,...
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Recurrent Neural Networks for Speech Recognition
Nováčik, Tomáš ; Karafiát, Martin (referee) ; Veselý, Karel (advisor)
This master thesis deals with the implementation of various types of recurrent neural networks via programming language lua using torch library. It focuses on finding optimal strategy for training recurrent neural networks and also tries to minimize the duration of the training. Furthermore various types of regularization techniques are investigated and implemented into the recurrent neural network architecture. Implemented recurrent neural networks are compared on the speech recognition task using AMI dataset, where they model the acustic information. Their performance is also compared to standard feedforward neural network. Best results are achieved using BLSTM architecture. The recurrent neural network are also trained via CTC objective function on the TIMIT dataset. Best result is again achieved using BLSTM architecture.
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Image Captioning with Recurrent Neural Networks
Kvita, Jakub ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
Tato práce se zabývá automatickým generovaním popisů obrázků s využitím několika druhů neuronových sítí. Práce je založena na článcích z MS COCO Captioning Challenge 2015 a znakových jazykových modelech, popularizovaných A. Karpathym. Navržený model je kombinací konvoluční a rekurentní neuronové sítě s architekturou kodér--dekodér. Vektor reprezentující zakódovaný obrázek je předáván jazykovému modelu jako hodnoty paměti LSTM vrstev v síti. Práce zkoumá, na jaké úrovni je model s takto jednoduchou architekturou schopen popisovat obrázky a jak si stojí v porovnání s ostatními současnými modely. Jedním ze závěrů práce je, že navržená architektura není dostatečná pro jakýkoli popis obrázků.
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