Multimodal Name Recognition in Live TV Subtitling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952588" target="_blank" >RIV/49777513:23520/18:43952588 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1748.html" target="_blank" >https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1748.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2018-1748" target="_blank" >10.21437/Interspeech.2018-1748</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multimodal Name Recognition in Live TV Subtitling

Popis výsledku v původním jazyce

In this paper, we present a method of combining a visual text reader with a system of automatic speech recognition to suppress errors when encountering out-of-vocabulary words – specifically names. The visual text reader outputs detected words that are mapped into a large list of names via the Levenshtein distance. The detected names are inserted into the class-based language model on the fly which improves recognition results. To demonstrate the effect on the real speech recognition task we use data from sports TV broadcasting where a lot of names are present in both the audio and video streams. We superseded manual vocabulary editing in live TV subtitling through re-speaking by an automated online process. Further, we show that automatically adding the names to the recognition vocabulary online and with forgetting lowers the WER relatively by 39 % in comparison with the case when names of all sportsmen are added to the vocabulary beforehand and by 15 % when only the relevant names are added beforehand.

Název v anglickém jazyce

Multimodal Name Recognition in Live TV Subtitling

Popis výsledku anglicky

In this paper, we present a method of combining a visual text reader with a system of automatic speech recognition to suppress errors when encountering out-of-vocabulary words – specifically names. The visual text reader outputs detected words that are mapped into a large list of names via the Levenshtein distance. The detected names are inserted into the class-based language model on the fly which improves recognition results. To demonstrate the effect on the real speech recognition task we use data from sports TV broadcasting where a lot of names are present in both the audio and video streams. We superseded manual vocabulary editing in live TV subtitling through re-speaking by an automated online process. Further, we show that automatically adding the names to the recognition vocabulary online and with forgetting lowers the WER relatively by 39 % in comparison with the case when names of all sportsmen are added to the vocabulary beforehand and by 15 % when only the relevant names are added beforehand.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 19th Annual Conference of the International Speech Communication Association (Interspeech 2018)

ISBN

978-1-5108-7221-9

ISSN

2308-457X

e-ISSN

neuvedeno

Počet stran výsledku

4

Strana od-do

3529-3532

Název nakladatele

Curran Associates, Inc.

Místo vydání

Red Hook, NY

Místo konání akce

Hyderabad, Indie

Datum konání akce

2. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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