Speaker Verification based on extraction of Deep Features
Article No.: 36, Pages 1 - 4
Abstract
In this paper we present an approach for speaker verification, based on the the extraction of deep features. More specifically, we propose a scheme that is based on a convolutional neural network. For audio representation we opt for spectrograms, i.e., images that result from the spectral content of voices. Our network is trained to extract visual features from these spectrograms. We demonstrate that our network is able to produce discriminative features for the problem at hand, and moreover, when transfer learning is used, few samples may be needed for accurate speaker verification.
References
[1]
Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. 2008. Speeded-up robust features (SURF). Computer vision and image understanding 110, 3 (2008), 346--359.
[2]
Matthew Brown, Gang Hua, and Simon Winder. 2011. Discriminative learning of local image descriptors. IEEE transactions on pattern analysis and machine intelligence 33, 1 (2011), 43--57.
[3]
Najim Dehak, Patrick J Kenny, Réda Dehak, Pierre Dumouchel, and Pierre Ouellet. 2011. Front-end factor analysis for speaker verification. IEEE Transactions on Audio, Speech, and Language Processing 19, 4 (2011), 788--798.
[4]
Martin A Fischler and Robert C Bolles. 1987. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In Readings in computer vision. Elsevier, 726--740.
[5]
Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. 249--256.
[6]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[7]
Felix Kreuk, Yossi Adi, Moustapha Cisse, and Joseph Keshet. 2018. Fooling End-to-end Speaker Verification by Adversarial Examples. arXiv preprint arXiv:1801.03339 (2018).
[8]
Yuan Liu, Yanmin Qian, Nanxin Chen, Tianfan Fu, Ya Zhang, and Kai Yu. 2015. Deep feature for text-dependent speaker verification. Speech Communication 73 (2015), 1--13.
[9]
David G Lowe. 2004. Distinctive image features from scale-invariant keypoints. International journal of computer vision 60, 2 (2004), 91--110.
[10]
Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2010), 1345--1359.
[11]
Gueorgui Pironkov, Stephane Dupont, and Thierry Dutoit. 2016. Multi-task learning for speech recognition: an overview. In Proceedings of the 24th European Symposium on Artificial Neural Networks (ESANN), Vol. 192.
[12]
Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, and Francesc Moreno-Noguer. 2015. Discriminative learning of deep convolutional feature point descriptors. In Computer Vision (ICCV), 2015 IEEE International Conference on. IEEE, 118--126.
[13]
Ehsan Variani, Xin Lei, Erik McDermott, Ignacio Lopez Moreno, and Javier Gonzalez-Dominguez. 2014. Deep neural networks for small footprint text-dependent speaker verification. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 4052--4056.
[14]
Xiu Zhang, Bilei Zhu, Linwei Li, Wei Li, Xiaoqiang Li, Wei Wang, Peizhong Lu, and Wenqiang Zhang. 2015. SIFT-based local spectrogram image descriptor: a novel feature for robust music identification. EURASIP Journal on Audio, Speech, and Music Processing 2015, 1 (2015), 6.
- Speaker Verification based on extraction of Deep Features
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Published In
July 2018
339 pages
ISBN:9781450364331
DOI:10.1145/3200947
Copyright © 2018 ACM.
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In-Cooperation
- EETN: Hellenic Artificial Intelligence Society
- UOP: University of Patras
- University of Thessaly: University of Thessaly, Volos, Greece
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 July 2018
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SETN '18
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