Adversarial manifold learning for speaker recognition

Jen-Tzung Chien, Kang Ting Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper presents an adversarial manifold learning (AML) for speaker recognition based on the probabilistic linear discriminant analysis (PLDA) using i-vectors. PLDA basically consists of an encoder for finding the latent variables and a decoder for reconstructing the i-vectors. AML is developed and incorporated in deep learning for a latent variable model. Low-dimensional latent space is therefore constructed according to an adversarial learning with neighbor embedding. This AML-PLDA is formulated to jointly optimize three learning objectives including a reconstruction error based on PLDA, a subspace learning for neighbor embedding and an adversarial loss caused by a discriminator and a generator. Using the deep neural networks, the generator is trained to fool the discriminator with its generated samples in latent space. The parameters in encoder, decoder and discriminator are jointly estimated by using the stochastic gradient descent algorithm. The experiments on speaker recognition show the merit of AML-PLDA in manifold learning and pattern classification.

Original languageEnglish
Title of host publication2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages599-605
Number of pages7
ISBN (Electronic)9781509047888
DOIs
StatePublished - 24 Jan 2018
Event2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Okinawa, Japan
Duration: 16 Dec 201720 Dec 2017

Publication series

Name2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings
Volume2018-January

Conference

Conference2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017
CountryJapan
CityOkinawa
Period16/12/1720/12/17

Keywords

  • Probabilistic linear discriminant analysis
  • adversarial learning
  • manifold learning
  • speaker recognition

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  • Cite this

    Chien, J-T., & Peng, K. T. (2018). Adversarial manifold learning for speaker recognition. In 2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings (pp. 599-605). (2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASRU.2017.8268991