Chien, J. and Hsu, C. (2017) Variational Manifold Learning for Speaker Recognition. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, 5-9 March 2017, 4935-4939.
https://doi.org/10.1109/ICASSP.2017.7953095

has been cited by the following article:

• TITLE: Locality Preserving Discriminant Projection for Speaker Verification

  AUTHORS: Chunyan Liang, Wei Cao, Shuxin Cao

  KEYWORDS: Speaker Verification, Locality Preserving Discriminant Projection, Locality Preserving Projection, Manifold Learning, Total Variability Factor Analysis

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.11, November 11, 2020

  ABSTRACT: In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.