Unsupervised synchronization of hidden subtitles with audio track using keyword spotting algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43916104" target="_blank" >RIV/49777513:23520/12:43916104 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-32790-2_51" target="_blank" >http://dx.doi.org/10.1007/978-3-642-32790-2_51</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-32790-2_51" target="_blank" >10.1007/978-3-642-32790-2_51</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised synchronization of hidden subtitles with audio track using keyword spotting algorithm

Popis výsledku v původním jazyce

This paper deals with a processing of hidden subtitles and with an assignment of subtitles without time alignment to the corresponding parts of audio records. The first part of this paper describes processing of hidden subtitles using a software framework designed for handling large volumes of language modelling data. It evaluates characteristics of a corpus built from publicly available subtitles and compares them with the corpora created from other sources of data such as news articles. The corpus consistency and similarity to other data sources is evaluated using a standard Spearman rank correlation coefficients. The second part presents a novel algorithm for unsupervised alignment of hidden subtitles to the corresponding audio. The algorithm uses no prior time alignment information. The method is based on a keyword spotting algorithm. This algorithm is used for approximate alignment, because large amount of redundant information is included in obtained results. The longest common s

Název v anglickém jazyce

Unsupervised synchronization of hidden subtitles with audio track using keyword spotting algorithm

Popis výsledku anglicky

This paper deals with a processing of hidden subtitles and with an assignment of subtitles without time alignment to the corresponding parts of audio records. The first part of this paper describes processing of hidden subtitles using a software framework designed for handling large volumes of language modelling data. It evaluates characteristics of a corpus built from publicly available subtitles and compares them with the corpora created from other sources of data such as news articles. The corpus consistency and similarity to other data sources is evaluated using a standard Spearman rank correlation coefficients. The second part presents a novel algorithm for unsupervised alignment of hidden subtitles to the corresponding audio. The algorithm uses no prior time alignment information. The method is based on a keyword spotting algorithm. This algorithm is used for approximate alignment, because large amount of redundant information is included in obtained results. The longest common s

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Artificial Intelligence

ISSN

0302-9743

e-ISSN

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Svazek periodika

7499

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

422-430

Kód UT WoS článku

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EID výsledku v databázi Scopus

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