Abstract
To apply a joint factor analysis (JFA) in a multiple channel circumstance, this paper proposes an orthogonal subspace combination method for a text-independent speaker recognition system. On the condition of multiple channels, the subspace loading matrix estimated by a mixed data corpus suffers from the data masking effects. And the subspace loading matrix estimated by a simple combination method has a drawback of subspace overlapping. To overcome these problems, this paper presents an orthogonal subspace combination method. The proposed method is based on a proper approximation of the core computation of the JFA and makes use of the Gram-Schmidt orthogonalization. On the NIST SRE 2008 core tasks corpus, the proposed method has a better performance.
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He, L., Liu, J. (2012). Orthogonal Subspace Combination Based on the Joint Factor Analysis for Text-Independent Speaker Recognition. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_30
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DOI: https://doi.org/10.1007/978-3-642-35136-5_30
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