The Influence of Errors in Phonetic Annotations on Performance of Speech Recognition System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F18%3A00006129" target="_blank" >RIV/46747885:24220/18:00006129 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-00794-2_45" target="_blank" >http://dx.doi.org/10.1007/978-3-030-00794-2_45</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-00794-2_45" target="_blank" >10.1007/978-3-030-00794-2_45</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Influence of Errors in Phonetic Annotations on Performance of Speech Recognition System

Popis výsledku v původním jazyce

This paper deals with errors in acoustic training data and the influence on speech recognition performance. The training data can be prepared manually, automatically or by combination of these two. In all cases, some mislabeled phonemes can appear in phonetic annotations. We conducted series of experiments which simulate some common errors. The experiments deal with various amount of changes in phonetic annotations such as different types of changes in voicing of obstruents, random substitution of consonants or vowels and random deleting of phonemes. All experiments were done for Czech language using GlobalPhone speech data set and both Gaussian mixture models and deep neural networks were used for acoustic modeling. The results show that some amount of such errors in training data does not influence speech recognition accuracy. The accuracy is significantly influenced only by large amount of errors (more than 50%)

Název v anglickém jazyce

The Influence of Errors in Phonetic Annotations on Performance of Speech Recognition System

Popis výsledku anglicky

This paper deals with errors in acoustic training data and the influence on speech recognition performance. The training data can be prepared manually, automatically or by combination of these two. In all cases, some mislabeled phonemes can appear in phonetic annotations. We conducted series of experiments which simulate some common errors. The experiments deal with various amount of changes in phonetic annotations such as different types of changes in voicing of obstruents, random substitution of consonants or vowels and random deleting of phonemes. All experiments were done for Czech language using GlobalPhone speech data set and both Gaussian mixture models and deep neural networks were used for acoustic modeling. The results show that some amount of such errors in training data does not influence speech recognition accuracy. The accuracy is significantly influenced only by large amount of errors (more than 50%)

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

Projekt

<a href="/cs/project/TH03010018" target="_blank" >TH03010018: DeepSpot - Multilingvální technologie pro detekci a včasné upozornění</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

2018

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) - 21st International Conference on Text, Speech, and Dialogue, TSD 2018

ISBN

978-303000793-5

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

419-427

Název nakladatele

Springer Verlag

Místo vydání

—

Místo konání akce

Brno, Czech republic

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—