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 ivector 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.
Cite as: Xu, L., Lee, K.A., Li, H., Yang, Z. (2016) Rapid Computation of I-vector. Proc. The Speaker and Language Recognition Workshop (Odyssey 2016), 47-52, doi: 10.21437/Odyssey.2016-7
@inproceedings{xu16_odyssey,
author={Longting Xu and Kong Aik Lee and Haizhou Li and Zhen Yang},
title={{Rapid Computation of I-vector}},
year=2016,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2016)},
pages={47--52},
doi={10.21437/Odyssey.2016-7}
}