Discriminative adaptation based on fast combination of DMAP and DfMLLR

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

Discriminative adaptation based on fast combination of DMAP and DfMLLR

Popis výsledku v původním jazyce

This paper investigates the combination of discriminative adaptation techniques. The discriminative Maximum A-Posteriori (DMAP) adaptation and discriminative feature Maximum Likelihood Linear Regression (DfMLLR) are examined. Since each of the methods isproposed for distinct amount of adaptation data it is useful to combine them in order to preserve the systems performance in situations with varying amount of adaptation data. Generally, DfMLLR and DMAP are executed subsequently (DMAP preceded by DfMLLR) demanding to approach the data twice. Since both methods address the data through the same statistics an one-pass-combination was proposed in order to decrease the time consumption. The one-pass-combination utilizes the advantage of DfMLLR method to transform directly the feature vectors. However, instead of feature vectors the statistics are transformed, what allows to use already computed statistics for the DMAP pass without the need to process the data once again. All the approaches

Název v anglickém jazyce

Discriminative adaptation based on fast combination of DMAP and DfMLLR

Popis výsledku anglicky

This paper investigates the combination of discriminative adaptation techniques. The discriminative Maximum A-Posteriori (DMAP) adaptation and discriminative feature Maximum Likelihood Linear Regression (DfMLLR) are examined. Since each of the methods isproposed for distinct amount of adaptation data it is useful to combine them in order to preserve the systems performance in situations with varying amount of adaptation data. Generally, DfMLLR and DMAP are executed subsequently (DMAP preceded by DfMLLR) demanding to approach the data twice. Since both methods address the data through the same statistics an one-pass-combination was proposed in order to decrease the time consumption. The one-pass-combination utilizes the advantage of DfMLLR method to transform directly the feature vectors. However, instead of feature vectors the statistics are transformed, what allows to use already computed statistics for the DMAP pass without the need to process the data once again. All the approaches

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

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)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Interspeech 2010

ISBN

978-1-61782-123-3

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

Curran Associates

Místo vydání

Red Hook

Místo konání akce

Makuhari, Chiba, Japonsko

Datum konání akce

1. 1. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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