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- W2071907278 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." @default.
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- W2071907278 date "2005-03-28" @default.
- W2071907278 modified "2023-09-23" @default.
- W2071907278 title "Add prior knowledge to speaker recognition" @default.
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- W2071907278 doi "https://doi.org/10.1117/12.603278" @default.
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