Factor analysis based spatial correlation modeling for speaker verification

Gaussian mixture models (GMMs) are commonly used in text-independent speaker verification for modeling the spectral distribution of speech. Recent studies have shown the effectiveness of characterizing speaker information using the mean super-vector obtained by concatenating the mean vectors of the GMM. This paper proposes to use the spatial correlation captured by the covariance matrix of the mean super-vector for speaker verification. Factor analysis method is adopted to estimate the covariance of the super-vector. For measuring the similarity between speech utterances in terms of the spatial correlation, we propose two kernel metrics, namely, log-Euclidean inner product and Frobenius angle. For computational simplicity, we introduce an inner product classifier (IPC) with equivalent performance compared to the commonly used support vector machine (SVM). Experiments conducted on the 2006 NIST speaker recognition evaluation (SRE) dataset confirm the efficacy of the proposed factor analysis based spatial modeling technique.