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The paper deals with the sociolinguistic concept of prestige imbued in the notion of standard language, and the social status connected to the inherent language skill (or lack thereof). To this end, we analyse Slovenian tweets pertaining to language use and the (in-)correctness of other users' use of language, propose a typology, especially in cases where language use is used as an argument against someone's qualifications or beliefs.

The article deals with language policy and language planning roles of translations and the translation industry/activity before and after Slovenia's accession to the EU, and highlights the effect of the newly gained change in the status of Slovenian. We place translation in the multilingual and multicultural context of the EU as a means of democratisation, and present the main challenges facing the Slovenian translation community in its efforts to make Slovenian an official language of the EU - many of these challenges continue today. V članku predstavljamo jezikovnopolitične in jezikovnonačrtovalske vloge, ki sta jih imela prevodno besedilo in prevodna dejavnost pred pristopom Slovenije k Evropski uniji in po njej, obenem pa poskušamo izpostaviti prvine, ki so se s spremembo statusa slovenskega jezika zanj tudi spremenile. Ob tem v kontekstu izrazite večjezikovnosti in večkulturnosti, ki ga prinaša Evropska unija, postavljamo vlogo prevoda kot temeljnega demokratičnega izenačevalca, obenem pa predstavljamo tudi konkretne izzive, s katerimi se je soočala stroka pri vzpostavljanju slovenskega jezika kot uradnega jezika Evropske unije, in izzive, s katerimi se sooča še zdaj.

Ob današnjem tehnološkem napredku na področju zagotavljanja jezikovnih in prevajalskih storitev je pomembno, da temu sledi tudi univerzitetno študijsko okolje. Temu je namenjen projekt oz. proces razvoja sistema za strojno prevajanje predavanj Online Notes (ON), ki ga obravnava to magistrsko delo. Namen dela je predstaviti osnovne značilnosti delovanja sistema ON, ugotoviti, kako dobro deluje na trenutni stopnji razvoja in postaviti temelje za nadaljnje ocenjevanje njegove uspešnosti v prihodnje. Analizirano je delovanje sistema, ki je razdeljeno na tri stopnje – razpoznava govora v slovenščini, prevajanje besedila iz slovenščine v angleščino, in prikaz besedila v obliki podnapisov, ki je namenjen končnim uporabnikom. Pri prvi stopnji so analizirane napake v prepoznavanju in pretvorbi govora s pomočjo metode podrobne ročne evalvacije. Enako metodo smo nato uporabili pri evalvaciji prevodov, ki je bila poleg tega opravljena tudi strojno, in sicer z uporabo ocenjevalnih metrik BLEU in METEOR. Odzive končnih uporabnikov pri tretji stopnji smo preverili s pomočjo anketnega vprašalnika. Pokazalo se je, da so pri razpoznavi govora potrebne določene izboljšave, predvsem pri segmentaciji stavkov. Napake so posledično vplivale tudi na slabšo kvaliteto prevodov, kljub temu pa so rezultati prevajalnika znotraj sistema ON v primerjavi s prevajalnikoma Google in DeepL solidni. To potrjujejo tudi odzivi respondentov v anketnem vprašalniku. Rezultati raziskave kažejo, da je razvoj sistema na dobri poti in da lahko ob primernih izboljšavah pričakujemo kvaliteten sistem za strojno prevajanje predavanj, ki bo primeren za uporabo v predavalnicah. With today's technological advances in language and translation services, it is important that the university learning environment keeps up with the times. This is the aim of the project for the development of Online Notes (ON), an automatic machine translation system for lectures. The purpose of this thesis is to present the basic features of the ON system, to determine how well it is working at its current stage of development and to lay the foundations for further evaluation of performance of the system in the future. The analysis of the system's performance is divided into three stages – automatic speech recognition (ASR) in Slovene, neural machine translation (NMT) from Slovene to English, and display of the text in subtitle form for end-users. At the first stage, errors in automatic speech recognition are analysed using a detailed manual evaluation method. The same method is applied in the evaluation of machine translation, which was also done automatically using the BLEU and METEOR evaluation metrics. Evaluations by the end-users were collected by means of a questionnaire. Results show that improvements are needed in speech recognition, and especially in sentence segmentation. Consequently, segmentation errors resulted in a lower quality of the translations, but the results of the ON system’s translator are solid compared to the Google and DeepL translators. This was also confirmed by the respondents' answers. Final results show that the development of the system is well on track and that, with appropriate improvements, we can expect an effective system for machine translation of lectures that will be suitable for use in lecture theatres.