The Impact of Inaccurate Phonetic Annotations on Speech Recognition Performance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F17%3A00004818" target="_blank" >RIV/46747885:24220/17:00004818 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-64206-2_45" target="_blank" >http://dx.doi.org/10.1007/978-3-319-64206-2_45</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-64206-2_45" target="_blank" >10.1007/978-3-319-64206-2_45</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Impact of Inaccurate Phonetic Annotations on Speech Recognition Performance

Popis výsledku v původním jazyce

This paper focuses on impact of phonetic inaccuracies of acoustic training data on performance of automatic speech recognition system. This is especially important if the training data is created in automated way. In this case, the data often contains errors in a form of wrong phonetic transcriptions. A series of experiments simulating various common errors in phonetic transcriptions based on parts of GlobalPhone data set (for Croatian, Czech and Russian) is conducted. These experiments show the influence of various errors on different languages and acoustic models (Gaussian mixture models, deep neural networks). The impact of errors is also shown for real data obtained by our automated ASR creation process for Belarusian. The results show that the best performance is achieved by using the most accurate data; however, certain amount of errors (up to 5%) does have relatively small impact on speech recognition accuracy

Název v anglickém jazyce

The Impact of Inaccurate Phonetic Annotations on Speech Recognition Performance

Popis výsledku anglicky

This paper focuses on impact of phonetic inaccuracies of acoustic training data on performance of automatic speech recognition system. This is especially important if the training data is created in automated way. In this case, the data often contains errors in a form of wrong phonetic transcriptions. A series of experiments simulating various common errors in phonetic transcriptions based on parts of GlobalPhone data set (for Croatian, Czech and Russian) is conducted. These experiments show the influence of various errors on different languages and acoustic models (Gaussian mixture models, deep neural networks). The impact of errors is also shown for real data obtained by our automated ASR creation process for Belarusian. The results show that the best performance is achieved by using the most accurate data; however, certain amount of errors (up to 5%) does have relatively small impact on speech recognition accuracy

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/TA04010199" target="_blank" >TA04010199: MULTILINMEDIA - Multilinguální platforma pro monitoring a analýzu multimédií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

9783319642055

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

402-410

Název nakladatele

Springer Verlag

Místo vydání

Německo

Místo konání akce

Praha, Česká Republika

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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