On the Complementary Role of DNN Multi-Level Enhancement for Noisy Robust Speaker Recognition in an I-Vector Framework
Author:
Affiliation:
1. Lab of Intelligent Information Processing, Army Engineering University
2. College of Communication Engineering, Army Engineering University
3. Institution of Information & Communication, National University of Defense Technology
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing
Link
https://www.jstage.jst.go.jp/article/transfun/E103.A/1/E103.A_2019EAL2104/_pdf
Reference17 articles.
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2. [2] O. Plchot, L. Burget, H. Aronowitz, and P. Matejka, “Audio enhancing with DNN autoencoder for speaker recognition,” Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol.1, pp.5090-5094, Shanghai, China, March 2016. 10.1109/icassp.2016.7472647
3. [3] S. Du, X. Xiao, and E.S. Chng, “DNN feature compensation for noise robust speaker verification,” Proc. IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), vol.1, pp.871-875, Chengdu, China, July 2015. 10.1109/chinasip.2015.7230529
4. [4] S. Mahto, H. Yamamoto, and T. Koshinaka, “I-vector transformation using a novel discriminative denoising autoencoder for noise-robust speaker recognition,” Proc. Conference of the International Speech Communication Association (INTERSPEECH), vol.1, pp.3722-3726, Stockholm, Sweden, Aug. 2017. 10.21437/interspeech.2017-731
5. [5] J. Guo, N. Xu, K. Qian, Y. Shi, K. Xu, Y.N. Wu, and A. Alwan, “Deep neural network based i-vector mapping for speaker verification using short utterances,” Speech Commun., vol.105, pp.92-102, Dec. 2018. 10.1016/j.specom.2018.10.004
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1. Increasing the Robustness of i-vectors with Model Compensated First Order Statistics;Afyon Kocatepe University Journal of Sciences and Engineering;2023-03-01
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