Methods of Domain Adaptation for Speech Recognition

The goal of this thesis is to develop a complete pipeline of Automatic Speech recognition for the Czech language with a particular focus on effective adap- tation of the model across a variety of diverse domains. Due to the scarcity of training data, we introduce two approaches for data preparation. First, we segment a portion of our audio files in a fully unsupervised way and use them to train our baseline acoustic model. We then use this model for further refinement of the segments. With our data pipeline, we prepare over 1500 hours of training data for the Czech language, from which 444 hours are made available to the public under a non-restrictive license. For our experiments, we use the hybrid acoustic model that combines the Gaus- sian Mixture Model and Hidden Markov Model with Neural Network-based methods. We also present our approach to language modeling in which we hier- archically combine interpolated n-gram models and a recurrent neural network model used to re-score the output lattices. Experiments with acoustic adap- tation, which finetune the neural network to a small amount of target domain audios, are presented as well. Lastly, we introduce an efficient implementation of a model for sentence embeddings, which we use to query an extensive cor- pus database and condition the search on a...Naše práce si jako hlavní cíl klade vytvoření kompletního systému pro au- tomatické rozpoznávání řeči pro Český jazyk. Důraz je zejména kladen na doménové adaptace našich modelů na různorodé cílové domény. Z důvodu ne- dostatku trénovacích dat pro Český jazyk nejprve představujeme dva přístupy pro přípravu dat. Jako první nasegmentujeme naše data bez použití doménového akustického modelu. Takto připravená data použijeme pro natrénování aku- stického modelu. Tento model je poté použit pro zlepšení našich segmentů a k přípravě korpusu Poslanecké sněmovny, který čítá přes 1500 hodin. Z tohoto objemu jsme již zveřejnili 444 hodin pro volné použití bez žádných licenčních restrikcí. Pro naše experimenty používáme hybridní akustický model který kombinuje Gaussian Mixture model a Hidden Markov Model s přístupy založenými na neuronových sítích. V naší práci také představíme přístup který používáme k jazykovému modelování ve kterém hierarchicky kombinujeme n-gramové modely s rekurentními neuronovými jazykovými modely které se využívají k reskórování laticí produkovaných akustickým modelem. Jako další představujeme naše ex- perimenty s dotrénováváním neuronové sítě na malém...Institute of Formal and Applied LinguisticsÚstav formální a aplikované lingvistikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

ELITR Non-Native Speech Translation at IWSLT 2020

This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT. We select our primary candidates from a pool of pre-existing systems, develop a new end-toend general ASR system, and a hybrid ASR trained on non-native speech. The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set

Methods of Domain Adaptation for Speech Recognition

The goal of this thesis is to develop a complete pipeline of Automatic Speech recognition for the Czech language with a particular focus on effective adap- tation of the model across a variety of diverse domains. Due to the scarcity of training data, we introduce two approaches for data preparation. First, we segment a portion of our audio files in a fully unsupervised way and use them to train our baseline acoustic model. We then use this model for further refinement of the segments. With our data pipeline, we prepare over 1500 hours of training data for the Czech language, from which 444 hours are made available to the public under a non-restrictive license. For our experiments, we use the hybrid acoustic model that combines the Gaus- sian Mixture Model and Hidden Markov Model with Neural Network-based methods. We also present our approach to language modeling in which we hier- archically combine interpolated n-gram models and a recurrent neural network model used to re-score the output lattices. Experiments with acoustic adap- tation, which finetune the neural network to a small amount of target domain audios, are presented as well. Lastly, we introduce an efficient implementation of a model for sentence embeddings, which we use to query an extensive cor- pus database and condition the search on a..

Efficiency of Prague Stock Exchange Market using Markov Chains

The main intention of this thesis is to analyze the weak form efficiency of Prague Stock Exchange. We conduct our empirical analysis on daily, weekly and monthly return data of the PX index collected in time period 1994-2017. The theory of Markov chains is employed to decide whether the index returns follow a random walk, the evidence of weak form efficiency. Bayesian Informa- tion Criterion is used to establish the optimal order of the Markov chain, which is in turn tested against the order 0 by Likelihood ratio criterion. The model assumptions of time homogeneity, irreducibility and aperiodicity of transition probability matrix are validated. We reject the weak form efficiency for daily index returns and establish its optimal Markov chain order to be 1. The weak form efficiency is not rejected for weekly and monthly index returns so is the as- sumption of time homogeneity for the whole time period 1994-2017. We propose further analysis of daily returns for time period 2006-2017, which exploits the fact of the weak form inefficiency. Discussion of results and related literature is provided as well as the presentation of all contemplated methods.

Efektivita Pražské burzy pomocí Markovských řetězcu

The main intention of this thesis is to analyze the weak form efficiency of Prague Stock Exchange. We conduct our empirical analysis on daily, weekly and monthly return data of the PX index collected in time period 1994-2017. The theory of Markov chains is employed to decide whether the index returns follow a random walk, the evidence of weak form efficiency. Bayesian Informa- tion Criterion is used to establish the optimal order of the Markov chain, which is in turn tested against the order 0 by Likelihood ratio criterion. The model assumptions of time homogeneity, irreducibility and aperiodicity of transition probability matrix are validated. We reject the weak form efficiency for daily index returns and establish its optimal Markov chain order to be 1. The weak form efficiency is not rejected for weekly and monthly index returns so is the as- sumption of time homogeneity for the whole time period 1994-2017. We propose further analysis of daily returns for time period 2006-2017, which exploits the fact of the weak form inefficiency. Discussion of results and related literature is provided as well as the presentation of all contemplated methods. 1Hlavním cílem této práce je analýza slabé efektivity Burzy cenných papírů v Praze. Naše empirická analýza zkoumá denní, týdenní a měsíční data z časového období 1994-2017. K testování hypotézy náhodné procházky burzovního indexu PX, která je indikátorem slabé formy efektivity je použita teorie Markovských řetězců. K určení optimálního řádu Markovského řetězce používáme metodu Bayesova informačního kritéria. Tento optimální řád je posléze testován proti řádu 0 za použití metody poměrů nejvěryhodnějších odhadů. Předpoklady mod- elu časové homogenity, ireducibility a aperiodicity jsou ověřené. Na základě našich výsledků zamítneme slabou efektivitu trhu pro denní výdělky indexu PX a ustanovíme jejich optimální řád 1. Slabou efektivitu nezamítneme pro týdenní a měsíční data, stejně jako předpoklad časové homogenity pro celé časové období 1994-2017. V závěru práce navrhujeme technickou analýzu, která využívá neefektivity trhu pro denní data. Práce také zahrnuje diskuzi výsledků a srovnání s již publikovanou literaturou na dané téma. 1Institut ekonomických studiíInstitute of Economic StudiesFakulta sociálních vědFaculty of Social Science

Design and model of implementation of the event planning process into the Czechitas information system

The goal of this bachelor thesis is the analysis of the event planning process and the subsequent creation of an implementation design and model for the implementation of the process into the Czechitas information system. The theoretical part of the work introduces concepts associated with information systems and the topic of process and design modeling. Furthermore, the work contains an introduction to the company, evaluation of the current state of the information system and mapping of key business processes. The company is interested in expanding the information system to support the event planning process, therefore an analysis of the requirements for this expansion and the creation of the design and related models are performed. Requirements are created and analyzed by examining existing documented requirements and performing unstructured interviews with their creator, who then validates the individual results of the design phases by utilizing created models and the prototype. The result of this bachelor thesis is an implementation design, model, and an interactive prototype of the suggested event planning process implementation into the information system.Cílem této práce je analýza procesu plánování akcí a následné vytvoření návrhu a modelu implementace procesu do informačního systému společnosti Czechitas. V teoretické části práce přiblíží pojmy spojené s informačními systémy, problematiku procesního modelování a vytváření návrhu a modelů. Dále práce obsahuje představení společnosti, zhodnocení současného stavu informačního systému a zmapování klíčových procesů. Společnost má zájem rozšířit informační systém o podporu procesu plánování akcí. Následuje tedy analýza požadavků na toto rozšíření a samotná tvorba návrhu a s ním spojených modelů. Požadavky jsou zanalyzovány zkoumáním již existujících zdokumentovaných požadavků, nestrukturovanými rozhovory s jejich zadavatelem, který následně provádí validaci jednotlivých výstupů fází návrhu za využití modelů a prototypu. Výstupem je vytvořený návrh, model a interaktivní prototyp implementace procesu plánování akcí do informačního systému

Large Corpus of Czech Parliament Plenary Hearings

We present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 444 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio snippets making it suitable for both training and evaluation of automatic speech recognition (ASR) systems. The source language of the corpus is Czech, which makes it a valuable resource for future research as only a few public datasets are available for the Czech language

Large Corpus of Czech Parliament Plenary Hearings

We present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 1200 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio segments making it suitable for both training and evaluation of automatic speech recognition (ASR) systems. The source language of the corpus is Czech, which makes it a valuable resource for future research as only a few public datasets are available in the Czech language. We complement the data release with experiments of two baseline ASR systems trained on the presented data: the more traditional approach implemented in the Kaldi ASRtoolkit which combines hidden Markov models and deep neural networks (NN) and a modern ASR architecture implemented in Jaspertoolkit which uses deep NNs in an end-to-end fashion

A Speech Test Set of Practice Business Presentations with Additional Relevant Texts

We present a test corpus of audio recordings and transcriptions of presentations of students' enterprises together with their slides and web-pages. The corpus is intended for evaluation of automatic speech recognition (ASR) systems, especially in conditions where the prior availability of in-domain vocabulary and named entities is benefitable. The corpus consists of 39 presentations in English, each up to 90 seconds long, and slides and web-pages in Czech, Slovak, English, German, Romanian, Italian or Spanish. The speakers are high school students from European countries with English as their second language. We benchmark three baseline ASR systems on the corpus and show their imperfection

ELITR Non-Native Speech Translation at IWSLT 2020

This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT. We select our primary candidates from a pool of pre-existing systems, develop a new end-to-end general ASR system, and a hybrid ASR trained on non-native speech. The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set