Efficient approximated i-vector extraction

I-vectors are currently widely used by state-of-the-art speech processing systems for tasks such as speaker verification and language identification. A shortcoming of i-vector-based systems is that the i-vector extraction process is computationally expensive. In this paper we propose an efficient method to extract i-vectors approximately. The method normalizes the GMM counts to be similar across sessions. We validate our method empirically for the speaker verification task on five different datasets, both text independent and text dependent. A significant speedup was obtained with a very small degradation in accuracy compared to the standard exact method.