28 March 2005 Add prior knowledge to speaker recognition
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Abstract
Prior knowledge helps to make the speaker recognition system more reliable and robust. This paper presents a uniform framework of feature-level fusion to incorporate the prior knowledge for speaker recognition using gender information based on dynamic Bayesian network (DBN). DBNs are a new statistical approach, with the ability to handle hidden variables and missing data in a principled way with high extensibility. And thus, DBNs can describe the prior knowledge conveniently. Our contribution is to apply DBNs to construct a general feature-level fusion to combine the general acoustic feature like MFCC and prior information like gender into a single DBN for speaker identification. In our framework, gender information become additional observed data to influence both hidden variables and observed acoustic data. Experimental evaluation over a subnet of YOHO corpus show promising results.
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Dongdong Li, Dongdong Li, Yingchun Yang, Yingchun Yang, Zhaohui Wu, Zhaohui Wu, Ting Huang, Ting Huang, } "Add prior knowledge to speaker recognition", Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); doi: 10.1117/12.603278; https://doi.org/10.1117/12.603278
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