Conference paper

MOŠNER Ladislav, PLCHOT Oldřich, MATĚJKA Pavel, NOVOTNÝ Ondřej and ČERNOCKÝ Jan. Dereverberation and Beamforming in Robust Far-Field Speaker Recognition. In: Proceedings of Interspeech 2018. Hyderabad: International Speech Communication Association, 2018, pp. 1334-1338. ISSN 1990-9770. Available from: https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2306.html
Publication language:english
Original title:Dereverberation and Beamforming in Robust Far-Field Speaker Recognition
Title (cs):Dereverberace a směrování paprsku v robustním rozpoznávání mluvčího ze vzdálených mikrofonů
Pages:1334-1338
Proceedings:Proceedings of Interspeech 2018
Conference:Interspeech 2018
Place:Hyderabad, IN
Year:2018
URL:https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2306.html
Journal:Proceedings of Interspeech - on line, Vol. 2018, No. 9, BAIXAS, FR
ISSN:1990-9770
DOI:10.21437/Interspeech.2018-2306
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2018/mosner_interspeech2018_2306.pdf [PDF]
Keywords
speaker verification, beamforming, dereverberation, autoencoder
Annotation
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.
BibTeX:
@INPROCEEDINGS{
   author = {Ladislav Mo{\v{s}}ner and Old{\v{r}}ich Plchot and
	Pavel Mat{\v{e}}jka and Ond{\v{r}}ej Novotn{\'{y}}
	and Jan {\v{C}}ernock{\'{y}}},
   title = {Dereverberation and Beamforming in Robust
	Far-Field Speaker Recognition},
   pages = {1334--1338},
   booktitle = {Proceedings of Interspeech 2018},
   journal = {Proceedings of Interspeech - on line},
   volume = {2018},
   number = {9},
   year = {2018},
   location = {Hyderabad, IN},
   publisher = {International Speech Communication Association},
   ISSN = {1990-9770},
   doi = {10.21437/Interspeech.2018-2306},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=11843}
}

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