Časové zpracování pro výpočet příznaků v rozpoznávání řeči

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F03%3APU42701" target="_blank" >RIV/00216305:26230/03:PU42701 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Temporal processing for feature extraction in speech recognition

Popis výsledku v původním jazyce

Speech recognition is a booming research field, having large number of applications in telecommunications (especially mobile), automobile industry, consumer electronics, military and security, etc. Speech recognition systems are classically built from three basic blocks: feature extraction, acoustic matching and language modeling. While the last two are trained on data (annotated databases for acoustics and large speech corpora for the LM), feature extraction block is often neglected and most often, mell-frequency cepstral coefficients (MFCC) are used. This work concentrates on two techniques that should improve the feature extraction. The first one is temporal filtering of feature trajectories using filters designed on data using Linear Discriminant Analysis (LDA). This technique is shown to improve the recognition accuracy of isolated Czech words, confirming previous results on US-English obtained by our colleagues from OGI Portland. The second part of the work concentrates on more r

Název v anglickém jazyce

Temporal processing for feature extraction in speech recognition

Popis výsledku anglicky

Speech recognition is a booming research field, having large number of applications in telecommunications (especially mobile), automobile industry, consumer electronics, military and security, etc. Speech recognition systems are classically built from three basic blocks: feature extraction, acoustic matching and language modeling. While the last two are trained on data (annotated databases for acoustics and large speech corpora for the LM), feature extraction block is often neglected and most often, mell-frequency cepstral coefficients (MFCC) are used. This work concentrates on two techniques that should improve the feature extraction. The first one is temporal filtering of feature trajectories using filters designed on data using Linear Discriminant Analysis (LDA). This technique is shown to improve the recognition accuracy of isolated Czech words, confirming previous results on US-English obtained by our colleagues from OGI Portland. The second part of the work concentrates on more r

Druh

C - Kapitola v odborné knize

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2003

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Vědecké spisy VUT

ISBN

80-214-2395-1

Počet stran výsledku

30

Strana od-do

0-29

Počet stran knihy

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Název nakladatele

Publishing house of Brno University of Technology VUTIUM

Místo vydání

Brno

Kód UT WoS kapitoly

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