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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 ESIC 1.0 -- Europarl Simultaneous Interpreting Corpus Macháček, Dominik; Žilinec, Matúš; Bojar, Ondřej. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2021 BASE Show details 2 Lost in Interpreting: Speech Translation from Source or Interpreter? ... Macháček, Dominik; Žilinec, Matúš; Bojar, Ondřej. - : arXiv, 2021 BASE Show details 3 ELITR Multilingual Live Subtitling: Demo and Strategy ... Bojar, Ondřej; Macháček, Dominik; Sagar, Sangeet. - : ACL Anthology, 2021 BASE Show details 4 A Speech Test Set of Practice Business Presentations with Additional Relevant Texts Macháček, Dominik; Kratochvíl, Jonáš; Vojtěchová, Tereza; Bojar, Ondřej. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2019 Abstract: 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. Keyword: ASR; ASR evaluation; European non-native English; non-native English; speech and relevant texts; speech corpus; speech recognition; speech recognition evaluation URL: http://hdl.handle.net/11234/1-3023 BASE Hide details 5 Promoting the Knowledge of Source Syntax in Transformer NMT Is Not Needed ... Pham, Thuong-Hai; Macháček, Dominik; Bojar, Ondřej. - : arXiv, 2019 BASE Show details 6 Morphological and Language-Agnostic Word Segmentation for NMT ... Macháček, Dominik; Vidra, Jonáš; Bojar, Ondřej. - : arXiv, 2018 BASE Show details

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