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Information Journal Paper

Title

TEXT INDEPENDENT SPEAKER VERIFICATION USING STRUCTURAL ADAPTED GAUSSIAN MIXTURE MODEL

Pages

  85-104

Abstract

 In this paper Structural Gaussian Mixture Model (SGMM) method, which used for GMM-UBM algorithm speed-up in speaker verification system, investigated. Effects of some parameters in Structural Background Model (SBM) construction studied in detail and optimal values are used in- model creation. In addition a structure named "GMM identifier" proposed for processing output scores of SBM-SGMM structure. Simulation results demonstrate that SBM optimal structure combined with proposed score processing unit outperforms the base system with neural network while the proposed system complexity is less than neural network. Using proposed method a speed-up rate of 2.7 attained while system performance improved compared to GMM-UBM system. Best configuration leads to an Equal Error Rate of 0.35% having considerable improvement considering GMM-UBM system performance of 1.71% in Equal Error Rate prospective.

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  • Cite

    APA: Copy

    SAEIDI, R., & SADEGHMOHAMMADI, H.R.. (2009). TEXT INDEPENDENT SPEAKER VERIFICATION USING STRUCTURAL ADAPTED GAUSSIAN MIXTURE MODEL. SIGNAL AND DATA PROCESSING, -(2 (SERIAL 10)), 85-104. SID. https://sid.ir/paper/160764/en

    Vancouver: Copy

    SAEIDI R., SADEGHMOHAMMADI H.R.. TEXT INDEPENDENT SPEAKER VERIFICATION USING STRUCTURAL ADAPTED GAUSSIAN MIXTURE MODEL. SIGNAL AND DATA PROCESSING[Internet]. 2009;-(2 (SERIAL 10)):85-104. Available from: https://sid.ir/paper/160764/en

    IEEE: Copy

    R. SAEIDI, and H.R. SADEGHMOHAMMADI, “TEXT INDEPENDENT SPEAKER VERIFICATION USING STRUCTURAL ADAPTED GAUSSIAN MIXTURE MODEL,” SIGNAL AND DATA PROCESSING, vol. -, no. 2 (SERIAL 10), pp. 85–104, 2009, [Online]. Available: https://sid.ir/paper/160764/en

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