Multi-Condition Training for Unknown Environment Adaptation in Robust ASR Under Real Conditions

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Condition Training for Unknown Environment Adaptation in Robust ASR Under Real Conditions

Popis výsledku v původním jazyce

Automatic speech recognition (ASR) systems frequently work in noisy environment. As they are often trained on clean speech data, noise reduction or adaptation techniques are applied to decrease the influence of background disturbance. This paper analysesthe recognition performance within such adaptation when multi-condition training data from real environment are used for training initial models. Although the quality of such models can decrease with the presence of noise in the training material, theyare supposed to include initial information about noise and consequently support the adaptation procedure. Experimental results show significant improvement of the proposed training method in robust ASR task under unknown noisy conditions. The decrease by 29% and 14% in word error rate against the case with clean speech training data was reached for the non-adapted and adapted system respectively.

Název v anglickém jazyce

Multi-Condition Training for Unknown Environment Adaptation in Robust ASR Under Real Conditions

Popis výsledku anglicky

Automatic speech recognition (ASR) systems frequently work in noisy environment. As they are often trained on clean speech data, noise reduction or adaptation techniques are applied to decrease the influence of background disturbance. This paper analysesthe recognition performance within such adaptation when multi-condition training data from real environment are used for training initial models. Although the quality of such models can decrease with the presence of noise in the training material, theyare supposed to include initial information about noise and consequently support the adaptation procedure. Experimental results show significant improvement of the proposed training method in robust ASR task under unknown noisy conditions. The decrease by 29% and 14% in word error rate against the case with clean speech training data was reached for the non-adapted and adapted system respectively.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

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

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

Acta Polytechnica

ISSN

1210-2709

e-ISSN

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Svazek periodika

49

Číslo periodika v rámci svazku

2-3/2009

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

5

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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