Dereverberation and Beamforming in Robust Far-Field Speaker Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130795" target="_blank" >RIV/00216305:26230/18:PU130795 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2306.html" target="_blank" >https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2306.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2018-2306" target="_blank" >10.21437/Interspeech.2018-2306</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dereverberation and Beamforming in Robust Far-Field Speaker Recognition

Popis výsledku v původním jazyce

This paper deals with robust speaker verification (SV) in farfield sensing. The robustness is verified on a subset of NIST SRE 2010 corpus retransmitted in multiple real rooms of different acoustics and captured with multiple microphones. We experimented with various data preprocessing steps including different approaches to dereverberation and beamforming applied to ad-hoc microphone arrays. We found that significant improvements in accuracy can be achieved with neural network based generalized eigenvalue beamformer preceded by weighted prediction error dereverberation. We also explored the effect of data augmentation by adding various real or simulated room acoustic properties to the Probabilistic Linear Discriminant Analysis (PLDA) training dataset. As a result, we developed a speaker recognition system whose performance is stable across different room acoustic conditions. It yields 41.4% relative improvement in performance over the system without multi-channel processing tested on the cleanest microphone data. With the best combination of data preprocessing and augmentation, we obtained a performance close to the one we achieved with the original clean test data.

Název v anglickém jazyce

Dereverberation and Beamforming in Robust Far-Field Speaker Recognition

Popis výsledku anglicky

This paper deals with robust speaker verification (SV) in farfield sensing. The robustness is verified on a subset of NIST SRE 2010 corpus retransmitted in multiple real rooms of different acoustics and captured with multiple microphones. We experimented with various data preprocessing steps including different approaches to dereverberation and beamforming applied to ad-hoc microphone arrays. We found that significant improvements in accuracy can be achieved with neural network based generalized eigenvalue beamformer preceded by weighted prediction error dereverberation. We also explored the effect of data augmentation by adding various real or simulated room acoustic properties to the Probabilistic Linear Discriminant Analysis (PLDA) training dataset. As a result, we developed a speaker recognition system whose performance is stable across different room acoustic conditions. It yields 41.4% relative improvement in performance over the system without multi-channel processing tested on the cleanest microphone data. With the best combination of data preprocessing and augmentation, we obtained a performance close to the one we achieved with the original clean test data.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Interspeech 2018

ISBN

—

ISSN

1990-9772

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1334-1338

Název nakladatele

International Speech Communication Association

Místo vydání

Hyderabad

Místo konání akce

Hyderabad, India

Datum konání akce

2. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000465363900279