Conference paper

SILNOVA Anna, BURGET Lukáš and ČERNOCKÝ Jan. Alternative Approaches to Neural Network based Speaker Verification. In: Proceedings of Interspeech 2017. Stockholm: International Speech Communication Association, 2017, pp. 1572-1575. ISSN 1990-9772. Available from: http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1062.PDF
Publication language:english
Original title:Alternative Approaches to Neural Network based Speaker Verification
Title (cs):Alternativní přístupy k neuronovým sítím založené na ověřování řečníka
Pages:1572-1575
Proceedings:Proceedings of Interspeech 2017
Conference:Interspeech 2017
Place:Stockholm, SE
Year:2017
URL:http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1062.PDF
Journal:Proceedings of Interspeech, Vol. 2017, No. 08, FR
ISSN:1990-9772
DOI:10.21437/Interspeech.2017-1062
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2017/silnova_interspeech2017_IS171062.pdf [PDF]
Keywords
automatic speaker recognition, deep neural networks, bottleneck features
Annotation
This paper describes experiment with the standard ivector/ PLDA system trained on the different NN based features. The results are reported on female part of NIST SRE 2010, condition 5 (English telephone data).
Abstract
Just like in other areas of automatic speech processing, feature extraction based on bottleneck neural networks was recently found very effective for the speaker verification task. However, better results are usually reported with more complex neural network architectures (e.g. stacked bottlenecks), which are difficult to reproduce. In this work, we experiment with the so called deep features, which are based on a simple feed-forward neural network architecture. We study various forms of applying deep features to i-vector/PDA based speaker verification. With proper settings, better verification performance can be obtained by means of this simple architecture as compared to the more elaborate bottleneck features. Also, we further experiment with multi-task training, where the neural network is trained for both speaker recognition and senone recognition objectives. Results indicate that, with a careful weighting of the two objectives, multi-task training can result in significantly better performing deep features.
BibTeX:
@INPROCEEDINGS{
   author = {Anna Silnova and Luk{\'{a}}{\v{s}} Burget and Jan
	{\v{C}}ernock{\'{y}}},
   title = {Alternative Approaches to Neural Network based Speaker
	Verification},
   pages = {1572--1575},
   booktitle = {Proceedings of Interspeech 2017},
   journal = {Proceedings of Interspeech},
   volume = {2017},
   number = {08},
   year = {2017},
   location = {Stockholm, SE},
   publisher = {International Speech Communication Association},
   ISSN = {1990-9772},
   doi = {10.21437/Interspeech.2017-1062},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=11582}
}

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