Deep Learning and Online Speech Activity Detection for Czech Radio Broadcasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952599" target="_blank" >RIV/49777513:23520/18:43952599 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-00794-2_46" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-00794-2_46</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-00794-2_46" target="_blank" >10.1007/978-3-030-00794-2_46</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning and Online Speech Activity Detection for Czech Radio Broadcasting

Popis výsledku v původním jazyce

In this paper, enhancements of online speech activity detection (SAD) is presented. Our proposed approach combines standard signal processing methods and modern deep-learning methods which allows simultaneous training of the detector’s parts that are usually trained or designed separately. In our SAD, an NN-based early score computation system, an NN-based score smoothing system and proposed online decoding system were incorporated in a training process. Besides the CNN and DNN, spectral flux and spectral variance features are also investigated. The proposed approach was tested on a Czech Radio broadcasting corpus. The corpus was used for investigation supervised and also semisupervised machine learning.

Název v anglickém jazyce

Deep Learning and Online Speech Activity Detection for Czech Radio Broadcasting

Popis výsledku anglicky

In this paper, enhancements of online speech activity detection (SAD) is presented. Our proposed approach combines standard signal processing methods and modern deep-learning methods which allows simultaneous training of the detector’s parts that are usually trained or designed separately. In our SAD, an NN-based early score computation system, an NN-based score smoothing system and proposed online decoding system were incorporated in a training process. Besides the CNN and DNN, spectral flux and spectral variance features are also investigated. The proposed approach was tested on a Czech Radio broadcasting corpus. The corpus was used for investigation supervised and also semisupervised machine learning.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Umělá inteligence a uvažování</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

Text, Speech, and Dialogue 21st International Conference, TSD 2018, Brno, Czech Republic, September 11-14, 2018, Proceedings

ISBN

978-3-030-00793-5

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

8

Strana od-do

428-435

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Brno, Czech Republic

Datum konání akce

11. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—