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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F06%3A00000013%21RIV07-AV0-23520___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
This paper describes a LVCSR system for automatic online subtitling (closed captioning) of TV transmissions of the Czech Parliament meetings. The recognition system is based on Hidden Markov Models, lexical trees and bigram language model. The acoustic model is trained on 40 hours of parliament speech and the language model on more than 10M tokens of parliament speech trancriptions. The first part of the article is focused on text normalization and class-based language model preparation. The second part describes the recognition network and its decoding with respect to real-time operation demands using up to 100k vocabulary. The third part outlines the application framework allowing generation and displaying of subtitles for any audio/video source. Finally, experimental results obtained on parliament speeches with recognition accuracy varying from 80 to 95 % (according to the discussed topic) are reported and discussed. Rozpoznávací systém je založen na skrytých markovových modelech (HMM), lexikálních stromech a bigramovém jazykovém modelu. Akustický model je natrénován na 40 hodinách parlamentních schůzí a jazykový model na více než 10M slov přepisů parlamentních schůzí. První část článku se zabývá normalizací textu a přípravou třídového jazykového modelu. Druhá část popisuje rozpoznávací síť a její dekódování s ohledem práci v reálném čase se slovníkem až 100k slov. Třetí část nastiňuje strukturu aplikace umožňující generován This paper describes a LVCSR system for automatic online subtitling (closed captioning) of TV transmissions of the Czech Parliament meetings. The recognition system is based on Hidden Markov Models, lexical trees and bigram language model. The acoustic model is trained on 40 hours of parliament speech and the language model on more than 10M tokens of parliament speech trancriptions. The first part of the article is focused on text normalization and class-based language model preparation. The second part describes the recognition network and its decoding with respect to real-time operation demands using up to 100k vocabulary. The third part outlines the application framework allowing generation and displaying of subtitles for any audio/video source. Finally, experimental results obtained on parliament speeches with recognition accuracy varying from 80 to 95 % (according to the discussed topic) are reported and discussed.
dcterms:title
Automatic Online Subtitling of the Czech Parliament Meetings Automatické online titulkování parlamentních přenosů Automatic Online Subtitling of the Czech Parliament Meetings
skos:prefLabel
Automatické online titulkování parlamentních přenosů Automatic Online Subtitling of the Czech Parliament Meetings Automatic Online Subtitling of the Czech Parliament Meetings
skos:notation
RIV/49777513:23520/06:00000013!RIV07-AV0-23520___
n4:strany
501
n4:aktivita
n14:P
n4:aktivity
P(1QS101470516)
n4:cisloPeriodika
0
n4:dodaniDat
n8:2007
n4:domaciTvurceVysledku
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n11:J
n4:duvernostUdaju
n9:S
n4:entitaPredkladatele
n12:predkladatel
n4:idSjednocenehoVysledku
466411
n4:idVysledku
RIV/49777513:23520/06:00000013
n4:jazykVysledku
n18:eng
n4:klicovaSlova
ASR; online; subtitling; parliament; czech
n4:klicoveSlovo
n10:subtitling n10:parliament n10:online n10:czech n10:ASR
n4:kodStatuVydavatele
DE - Spolková republika Německo
n4:kontrolniKodProRIV
[095D44D867DE]
n4:nazevZdroje
Lecture Notes in Artificial Intelligence
n4:obor
n13:JD
n4:pocetDomacichTvurcuVysledku
6
n4:pocetTvurcuVysledku
6
n4:projekt
n16:1QS101470516
n4:rokUplatneniVysledku
n8:2006
n4:tvurceVysledku
Hoidekr, Jan Psutka, Josef Müller, Luděk Pražák, Aleš Kanis, Jakub
s:issn
0302-9743
s:numberOfPages
8
n17:organizacniJednotka
23520