Investigation on Most Frequent Errors in Large-Scale Speech Recognition Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F12%3A%230002009" target="_blank" >RIV/46747885:24220/12:#0002009 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Investigation on Most Frequent Errors in Large-Scale Speech Recognition Applications

Popis výsledku v původním jazyce

When automatic speech recognition (ASR) system is being developed for an application where a large amount of audio documents is to be transcribed, we need some feedback information that tells us, what the main types of errors are, why and where they occur and what can be done to eliminate them. While the algorithm commonly used for counting the number of word errors is simple, it does not care much about the nature and source of the errors. In this paper, we introduce a scheme that offers a more detailed insight into analysis of ASR errors. We apply it to the performance evaluation of a Czech ASR system whose main goal is to transcribe oral archives containing hundreds of thousands spoken documents. The analysis is performed by comparing 763 hours of manually and automatically transcribed data. We list the main types of errors and present methods that try to eliminate at least the most relevant ones. We show that the proposed error locating method can be useful also when porting an exi

Název v anglickém jazyce

Investigation on Most Frequent Errors in Large-Scale Speech Recognition Applications

Popis výsledku anglicky

When automatic speech recognition (ASR) system is being developed for an application where a large amount of audio documents is to be transcribed, we need some feedback information that tells us, what the main types of errors are, why and where they occur and what can be done to eliminate them. While the algorithm commonly used for counting the number of word errors is simple, it does not care much about the nature and source of the errors. In this paper, we introduce a scheme that offers a more detailed insight into analysis of ASR errors. We apply it to the performance evaluation of a Czech ASR system whose main goal is to transcribe oral archives containing hundreds of thousands spoken documents. The analysis is performed by comparing 763 hours of manually and automatically transcribed data. We list the main types of errors and present methods that try to eliminate at least the most relevant ones. We show that the proposed error locating method can be useful also when porting an exi

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/DF11P01OVV013" target="_blank" >DF11P01OVV013: Zpřístupnění archivu Českého rozhlasu pro sofistikované vyhledávání</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proc. of Text, Speech and Dialogue (TSD),

ISBN

978-3-642-32789-6

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

520-527

Název nakladatele

Springer, Berlin Heidelberg

Místo vydání

Berlín, Německo

Místo konání akce

Česká Republika

Datum konání akce

1. 1. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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