Speaker Verification Using MMAP Adaptation (言語理解とコミュニケーション)

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概要

• 論文の詳細を見る

• This paper proposes maximum a posteriori (MAP) adaptation of Gaussian mixture models (GMM) using multiple priors for text-independent speaker verification. Although the hierarchical prior used in structural MAP (SMAP) adaptation has been proven to outperform the prior used in relevance MAP adaptation, there may still be some complementary information in the relevance prior which might be useful. An idea of combining these two priors is introduced here in the MAP framework. We call this method multiprior MAP (MMAP). We evaluated our proposed method on NIST SRE 2006 10sec4w-10sec4w task. We compared MMAP with classical maximum likelihood (ML) estimation, relevance MAP and SMAP adaptation techniques and proved its effectiveness. We also investigated the effect of Z-Norm and T-Norm on the likelihood ratio scores of different systems here.

• 2011-12-12

著者

• Shinoda Koichi

  Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut

• Furui Sadaoki

  Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut

• Biswas Sangeeta

  Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institute Of Technology

• ROHDIN Johan

  Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology

• Rohdin Johan

  Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institute Of Technology

• BISWAS Sangeeta

  Department of Computer Science, Graduate School of Information Science and Engineering

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