Compensation of Nonlinear Distortions in Speech for Automatic Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F14%3A%230002972" target="_blank" >RIV/46747885:24220/14:#0002972 - 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

Compensation of Nonlinear Distortions in Speech for Automatic Recognition

Popis výsledku v původním jazyce

This paper addresses improvement of automatic transcription of speech distorted already during recording or by consequent processing. We focus on distortions that cannot be represented by most often used models, that is, as an additive noise or a linearconvolutive channel distortion. We consider a) signals distorted through overgained microphone preamplifier and b) recordings exhibiting unnatural spectral sparseness, caused by application of excessive denoising or low-bit-rate compression. We demonstrate that these distortions deteriorate ASR accuracy significantly. To compensate, we propose to employ a combination of two general robust speech recognition techniques: a front-end feature normalization method and a channel/speaker adaptation technique.We present a significant improvement of transcription accuracy in the case of lectures distorted during recording, compressed broadcast data and utterances recorded with an inappropriately applied denoising.

Název v anglickém jazyce

Compensation of Nonlinear Distortions in Speech for Automatic Recognition

Popis výsledku anglicky

This paper addresses improvement of automatic transcription of speech distorted already during recording or by consequent processing. We focus on distortions that cannot be represented by most often used models, that is, as an additive noise or a linearconvolutive channel distortion. We consider a) signals distorted through overgained microphone preamplifier and b) recordings exhibiting unnatural spectral sparseness, caused by application of excessive denoising or low-bit-rate compression. We demonstrate that these distortions deteriorate ASR accuracy significantly. To compensate, we propose to employ a combination of two general robust speech recognition techniques: a front-end feature normalization method and a channel/speaker adaptation technique.We present a significant improvement of transcription accuracy in the case of lectures distorted during recording, compressed broadcast data and utterances recorded with an inappropriately applied denoising.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/TA01011142" target="_blank" >TA01011142: Automatická transkripce a indexace přednášek</a><br>

Návaznosti

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

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proc. of Telecommunications and Signal Processing (TSP) conference

ISBN

978-80-214-4983-1

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

419-423

Název nakladatele

IEEE

Místo vydání

Berlín, Německo

Místo konání akce

Berlín, Německo

Datum konání akce

1. 1. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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