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GMM-UBM super-vectors will potentially lead to worse modelling for speaker verification due to the inter-session variability, especially when a small amount of training utterances were available. In this study, we propose a phoneme dependent method to suppress the inter-session variability. A speaker's model can be represented by several various phoneme Gaussian mixture models. Each of them covers an individual phoneme whose inter-session variability can be constrained in an inter-session independent subspace constructed by principal component analysis (PCA), and it uses corpus uttered by a single speaker that has been recorded over a long period. SVM-based experiments performed using a large corpus, constructed by the National Research Institute of Police Science (NRIPS) to evaluate Japanese speaker recognition, and demonstrate the improvements gained from the proposed method. Keywords: inter-session variability; phoneme; speaker verification; principal component analysis. Reference to this paper should be made as follows: Lu, H., Zhang, W., Horiuchi, Y. and Kuroiwa, S. (2015) `Phoneme dependent inter-session variability reduction for speaker verification', Int. J. Biometrics, Vol. 7, No. 2, pp.8396. Biographical notes: Haoze Lu received his BE in Electronic Commerce from Donghua, University, Shanghai, China. Currently, he is a doctor student of the Graduate School of Science and Technology,
International Journal of Biometrics – Inderscience Publishers
Published: Jan 1, 2015
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