Improved Estimation of Articulatory Features Based on Acoustic Features with Temporal Context

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00232103" target="_blank" >RIV/68407700:21230/15:00232103 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-24033-6_63" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-24033-6_63</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-24033-6_63" target="_blank" >10.1007/978-3-319-24033-6_63</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improved Estimation of Articulatory Features Based on Acoustic Features with Temporal Context

Popis výsledku v původním jazyce

The paper deals with neural network-based estimation of articulatory features for Czech which are intended to be applied within automatic phonetic segmentation or automatic speech recognition. In our current approach we use the multi-layer perceptron networks to extract the articulatory features on the basis of non-linear mapping from standard acoustic features extracted from speech signal. The suitability of various acoustic features and the optimum length of temporal context at the input of used network were analysed. The temporal context is represented by a context window created from the stacked feature vectors. The optimum length of the temporal contextual information was analysed and identified for the context window in the range from 9 to 21 frames.We obtained 90.5% frame level accuracy on average across all the articulatory feature classes for mellog filter-bank features. The highest classification rate of 95.3% was achieved for the voicing class.

Název v anglickém jazyce

Improved Estimation of Articulatory Features Based on Acoustic Features with Temporal Context

Popis výsledku anglicky

The paper deals with neural network-based estimation of articulatory features for Czech which are intended to be applied within automatic phonetic segmentation or automatic speech recognition. In our current approach we use the multi-layer perceptron networks to extract the articulatory features on the basis of non-linear mapping from standard acoustic features extracted from speech signal. The suitability of various acoustic features and the optimum length of temporal context at the input of used network were analysed. The temporal context is represented by a context window created from the stacked feature vectors. The optimum length of the temporal contextual information was analysed and identified for the context window in the range from 9 to 21 frames.We obtained 90.5% frame level accuracy on average across all the articulatory feature classes for mellog filter-bank features. The highest classification rate of 95.3% was achieved for the voicing class.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Text, Speech, and Dialogue. 18th International Conference, TSD 2015

ISBN

978-3-319-24032-9

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

560-568

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Plzen

Datum konání akce

14. 9. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000365947800063