Glottal Closure Instant Detection from Speech Signal Using Voting Classifier and Recursive Feature Elimination

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952587" target="_blank" >RIV/49777513:23520/18:43952587 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1147.html" target="_blank" >https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1147.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2018-1147" target="_blank" >10.21437/Interspeech.2018-1147</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Glottal Closure Instant Detection from Speech Signal Using Voting Classifier and Recursive Feature Elimination

Popis výsledku v původním jazyce

In our previous work, we introduced a classification-based method for the automatic detection of glottal closure instants (GCIs) from the speech signal and we showed it was able to perform very well on several test datasets. In this paper, we investigate whether adding more features (voiced/unvoiced, harmonic/noise, spectral etc.) and/or using an ensemble of classifiers such as a voting classifier can further improve GCI detection performance. We show that using additional features leads to a better detection accuracy; best results were obtained when recursive feature elimination was applied on the whole feature set. In addition, a voting classifier is shown to outperform other classifiers and other existing GCI detection algorithms on publicly available databases.

Název v anglickém jazyce

Glottal Closure Instant Detection from Speech Signal Using Voting Classifier and Recursive Feature Elimination

Popis výsledku anglicky

In our previous work, we introduced a classification-based method for the automatic detection of glottal closure instants (GCIs) from the speech signal and we showed it was able to perform very well on several test datasets. In this paper, we investigate whether adding more features (voiced/unvoiced, harmonic/noise, spectral etc.) and/or using an ensemble of classifiers such as a voting classifier can further improve GCI detection performance. We show that using additional features leads to a better detection accuracy; best results were obtained when recursive feature elimination was applied on the whole feature set. In addition, a voting classifier is shown to outperform other classifiers and other existing GCI detection algorithms on publicly available databases.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/TH02010307" target="_blank" >TH02010307: Automatická konzervace a rekonstrukce hlasu se zaměřením na pacienty po totální laryngektomii</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

5

Strana od-do

2112-2116

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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