Machine Translation and the Evaluation of Its Quality

Machine translation has already become part of our everyday life. This chapter gives an overview of machine translation approaches. Statistical machine translation was a dominant approach over the past 20 years. It brought many cases of practical use. It is described in more detail in this chapter. Statistical machine translation is not equally successful for all language pairs. Highly inflectional languages are hard to process, especially as target languages. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This chapter also describes the evaluation of machine translation quality. It covers manual and automatic evaluations. Traditional and recently proposed metrics for automatic machine translation evaluation are described. Human translation still provides the best translation quality, but it is, in general, time-consuming and expensive. Integration of human and machine translation is a promising workflow for the future. Machine translation will not replace human translation, but it can serve as a tool to increase productivity in the translation process

Avtomatsko razpoznavanja slovenskega govora za dnevnoinformativne oddaje

Na področju govornih in jezikovnih tehnologij predstavlja avtomatsko razpoznavanje govora enega izmed ključnih gradnikov. V prispevku bomo predstavili razvoj avtomatskega razpoznavalnika slovenskega govora za domeno dnevnoinformativnih oddaj. Arhitektura sistema je zasnovana na globokih nevronskih mrežah. Pri tem smo ob upoštevanju razpoložljivih govornih virov izvedli modeliranje z različnimi aktivacijskimi funkcijami. V postopku razvoja razpoznavalnika govora smo preverili tudi, kakšen je vpliv izgubnih govornih kodekov na rezultate razpoznavanja govora. Za učenje razpoznavalnika govora smo uporabili bazi UMB BNSI Broadcast News in IETK-TV. Skupni obseg govornih posnetkov je znašal 66 ur. Vzporedno z globokimi nevronskimi mrežami smo povečali slovar razpoznavanja govora, ki je tako znašal 250.000 besed. Na ta način smo znižali delež besed izven slovarja na 1,33 %. Z razpoznavanjem govora na testni množici smo dosegli najboljšo stopnjo napačno razpoznanih besed (WER) 15,17 %. Med procesom vrednotenja rezultatov smo izvedli tudi podrobnejšo analizo napak razpoznavanja govora na osnovi lem in F-razredov, ki v določeni meri pokažejo na zahtevnost slovenskega jezika za takšne scenarije uporabe tehnologije

Digital Structures and Systems

Gradivo vsebuje navodila za laboratorijske vaje pri predmetu Digitalne strukture in sistemi za študente prve stopnje univerzitetnega študijskega programa Elektrotehnika, smer Elektronika. Laboratorijske vaje obravnavajo začetno spoznavanje uporabe jezikov za opisovanje strojne opreme in uporabo programirljivih logičnih vezij. Vaje so tako primerne tudi za druge predmete s podobno vsebino. Vsebina vaj obsega implementacijo preprostih preklopnih vezij, implementacijo preprostih sekvenčnih vezij, simulacijo digitalnih vezij in primere nekoliko kompleksnejših uporabnih vezij. Pri teh primerih so obravnavane tudi druge teme, kot so povezovanja digitalnih sklopov oz. modulov, uporaba logičnega analizatorja in podrobnejša analiza delovanja načrtovanih vezij.This material contains the instructions for laboratory work in the subject Digital Structures and Systems for students of the university study program Electrical Engineering, module Electronics. The instructions cover the first steps in using hardware description languages and programmable logic devices. The instructions can also be used for other subjects with a similar syllabus. The instructions cover the implementation of simple combinatorial circuits, the implementation of simple sequential circuits, the simulation of digital circuits, and examples of somewhat more complex useful circuits. In these examples, other topics are covered, such as the interconnection of digital modules, the use of logic analyzers, and a more detailed analysis of the described circuits

Context-dependent factored language models

The incorporation of grammatical information into speech recognition systems is often used to increase performance in morphologically rich languages. However, this introduces demands for sufficiently large training corpora and proper methods of using the additional information. In this paper, we present a method for building factored language models that use data obtained by morphosyntactic tagging. The models use only relevant factors that help to increase performance and ignore data from other factors, thus also reducing the need for large morphosyntactically tagged training corpora. Which data is relevant is determined at run-time, based on the current text segment being estimated, i.e., the context. We show that using a context-dependent model in a two-pass recognition algorithm, the overall speech recognition accuracy in a Broadcast News application improved by 1.73% relatively, while simpler models using the same data achieved only 0.07% improvement. We also present a more detailed error analysis based on lexical features, comparing first-pass and second-pass results

RANDOM NUMBER GENERATOR BASED ON CHAOTIC OSCILLATOR

V diplomski nalogi je predstavljen generator naključnih števil, ki temelji na analognem kaotičnem vezju, in statistično testiranje njegove primernosti. Predstavljeni so osnovni pojmi iz teorije kaosa in generatorjev naključnih števil. Podrobneje so opisani tudi statistični testi, ki se uporabljajo za preverjanje ustreznosti generatorja naključnih števil za kriptografske namene. Izbrano je kaotično vezje in postopek pridobivanja naključnih števil iz njegovega obnašanja. Delovanje vezja je preverjeno s simulacijami, meritvami in statističnimi testi.This diploma work describes a random number generator based on an analogue chaotic circuit. Basic concepts from chaos theory and random number generators are presented. A description of statistical tests is also given. These tests are used for testing random number generator used for cryptographic purposes. A chaotic circuit and an algorithm for generating random numbers are chosen. The operation of the circuit is tested with simulations, measurements and statistical tests

Automatic speech recognition in an inflective language using morphological language models with context dependent structure

V nalogi smo se posvetili jezikovnemu modeliranju za avtomatsko razpoznavanje govora z velikim slovarjem besed. Pri takšnem razpoznavanju je še vedno velika težava pravilnost razpoznavanja izgovorjenih besed. Ta je še posebej izrazita pri morfološko kompleksnejših jezikih, kot je slovenščina. Za delovanje sistema razpoznavanja tekočega govora potrebujemo jezikovne modele. Da lahko zgradimo primeren jezikovni model, potrebujemo ustrezno velike učne množice podatkov, ki morajo pri morfološko kompleksnejših jezikih biti še večje. Sodobni razpoznavalniki govora za slovenščino delajo več napak kot razpoznavalniki za druge jezike. Pogost problem so napačno razpoznane končnice besed. To kaže, da je smiselno razmišljati o vključevanju oblikoskladenjskih informacij v jezikovno modeliranje, če hočemo zmanjšati število napak. V doktorski nalogi predstavljamo zasnovo sistema, ki ob običajnih n-gramskih besednih jezikovnih modelih uporablja tudi modele, ki vključujejo informacije o besedni vrsti in slovničnih kategorijah prepoznanih besed. Imenujemo jih morfološki modeli. Razvili smo algoritem, ki na osnovi rezultatov perpleksnosti na razvojni množici določa najprimernejšo strukturo takšnih modelov glede na besedne vrste konteksta besede, ki jo ocenjujemo. Pravimo, da imajo modeli kontekstno odvisno strukturo. Implementirali smo jih kot faktorizirane jezikovne modele. V teh modelih se soočamo z veliko množico različnih možnih kontekstov besede in za vsak kontekst gradimo strukturo modelov ločeno. Pri tem lahko uporabimo le majhen del učne množice. Zato prihaja tudi tukaj do pomanjkanja učnih podatkov, kljub temu da imamo manjše zahteve po velikosti učne množice. Zato smo razvili pristope združevanja različnih kontekstov. Zaradi velikega števila možnih kontekstov in veliko različnih možnosti struktur modelov smo razvili tudi pristope za omejeno iskanje možnih struktur modelov na podlagi postopne gradnje njihovih struktur in sprotnega ocenjevanja. Sistem razpoznavanja je zasnovan v obliki dvoprehodnega algoritma, kjer v drugem prehodu uporabljamo v okviru doktorske disertacije razvite modele. Razvili smo tudi postopek za hitro optimizacijo uteži modelov in postopek dinamičnega uteževanja glede na kontekst besede. Uspešnost razpoznavanja z razvitimi modeli in brez njih smo testirali na slovenski govorni bazi Broadcast News.In this thesis, we are focused on language modelling for automatic speech recognition in large vocabulary applications, where we are still experiencing the problem of insufficient recognition accuracy. This problem is more present in morphologically complex languages, for example Slovene. For such a system to work properly we need language models. State of the art speech recognition systems for Slovene still produce a hidher number of recognition errors that recognizers for other langauges. We see many sentences that are still understandable, but which contain syntactical errors. Often errors are present in the word endings. Therefore it seems reasonable to include morphosyntactic information into language models to reduce syntactical errors. This thesis presents the development of a speech recognition system that uses not only the usual n-gram language models for words, but also models that include part-of-speech and morphosyntactic information. We call them morphological models. We developed an algorithm that determines the best structure for such models based on perplexities for with respect to the part-of-speech categories of a words context. We say that the models have a context dependent structure. We implemented them as factored language models. Although we do not need a very large training corpus we still experience data sparsity due to the large number of possible context of a word. We therefore also developed a method for merging different context. Because of a large number of possible models structures it was also necessary to develop an algorithm for limiting the search space by gradually determining a models structure. The system is designed as a two-pass recognition algorithm, where the morphological models are used in the second pass. We developed an algorithm for a fast optimization of the systems parameters and dynamic weighting of the models scores based on a words context. We tested speech accuracy on the Slovene Broadcast news speech database. We also added a more detailed analysis of the recognition results

Solution algorithms for the three fundamental problems of hidden Markov models

V diplomski nalogi se obravnavajo prikriti markovski modeli, ki se v praksi uporabljajo predvsem na področju razpoznavanja govora. Opisani so osnovni pojmi modelov in trije osnovni z njimi povezani problemi: problem ocenjevanja, problem dekodiranja in problem učenja. Opisane so metode za reševanje teh problemov za diskretne in zvezne prikrite markovske modele. Na kratko je opisana tudi uporaba prikritih markovskih modelov v razpoznavanju govora na primeru enostavne aplikacije razpoznavanja izoliranih besed z majhnim slovarjem.This paper presents hidden Markov models, which are in practice mostly used in speech recognition. Basic terms of the models and their tree fundamental problems: the evaluation, the decoding and the training problem are described. So are methods for solving them in the cases of discrete and continuous Markov models. A short description of the use of hidden Markov models in speech recognition is also presented on the example application of small vocabulary isolated word recognition

Language modeling for automatic speech recognition of inflective languages: an applications-oriented approach using lexical data

This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages. Concentrates on speech recognition for inflective languages – representative of roughly half of Europe -- and their unique characteristics Introduces new application-oriented methods for measuring the performance of a speech recognition system Presents examples of language modeling to maximize the performance of a speech recognition system Provides techniques for analyzing errors and identifying their sources in a speech recognition system from a lexical point of view rather than acoustic point of view