Abstract
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 language | English |
---|---|
Pages | 47-52 |
Number of pages | 6 |
DOIs | |
Publication status | Published - Jun 2016 |
Externally published | Yes |
Event | Speaker and Language Recognition Workshop, Odyssey 2016 - Bilbao, Spain Duration: 21 Jun 2016 → 24 Jun 2016 |
Conference
Conference | Speaker and Language Recognition Workshop, Odyssey 2016 |
---|---|
Country/Territory | Spain |
City | Bilbao |
Period | 21/06/16 → 24/06/16 |
ASJC Scopus subject areas
- Signal Processing
- Software
- Human-Computer Interaction