Rapid computation of i-vector

Longting Xu, Kong Aik Lee, Haizhou Li, Zhen Yang

Research output: Unpublished conference presentation (presented paper, abstract, poster)Conference presentation (not published in journal/proceeding/book)Academic researchpeer-review

7 Citations (Scopus)


I-vector has been one of the state-of-the-art techniques in speaker recognition. The main computational load of the standard i-vector extraction is to evaluate the posterior covariance matrix, which is required in estimating the i-vector. This limits the potential use of i-vector on handheld devices and for large-scale cloud-based applications. Previous fast approaches focus on simplifying the posterior covariance computation. In this paper, we propose a method for rapid computation of i-vector which bypasses the need to evaluate a full posterior covariance thereby speeds up the extraction process with minor impact on the recognition accuracy. This is achieved by the use of subspace-orthonormalizing prior and the uniform-occupancy assumption that we introduce in this paper. From the experiments conducted on the extended core task of NIST SRE'10, we obtained significant speed-up with modest degradation in performance over the standard i-vector.

Original languageEnglish
Number of pages6
Publication statusPublished - Jun 2016
Externally publishedYes
EventSpeaker and Language Recognition Workshop, Odyssey 2016 - Bilbao, Spain
Duration: 21 Jun 201624 Jun 2016


ConferenceSpeaker and Language Recognition Workshop, Odyssey 2016

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Human-Computer Interaction


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