Using regularized fisher discriminant analysis to improve the performance of Gaussian supervector in session and device identification

Hung Fat Frank Leung, Yuechi Jiang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)


In this paper, we propose Regularized Fisher Discriminant Analysis (RFDA) as a projection method applied on Gaussian Supervector (GSV). GSV was originally applied on speaker recognition and verification, and has exhibited good performance. Recently GSV has also been applied in audio forensics area, such as recording device identification. It has been shown that GSV can also capture useful information related to the recording device. In this paper, we show that GSV can also be applied in telephone session identification. However, although GSV can capture useful information for different identification purposes, the performance of the raw GSV may not be so good. Thus, we apply RFDA-based projection method on the raw GSV, and find that this projection method can significantly improve the performance of the raw GSV, in both telephone session identification and recording device identification tasks.
Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Number of pages8
ISBN (Electronic)9781509061815
Publication statusPublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017


Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States


  • Audio forensics
  • Projected Gaussian Supervector
  • Recording device identification
  • Regularized Fisher Discriminant Analysis
  • Telephone session identification

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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