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Fusion of SNR-Dependent PLDA Models for Noise Robust Speaker Verification

Fusion of SNR-Dependent PLDA Models for Noise Robust Speaker Verification. Xiaomin PANG and Man-Wai MAK Dept . of Electronic and Information Engineering, The Hong Kong Polytechnic University. Introduction Motivation

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Fusion of SNR-Dependent PLDA Models for Noise Robust Speaker Verification

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  1. Fusion of SNR-Dependent PLDA Models for Noise Robust Speaker Verification Xiaomin PANG and Man-Wai MAK • Dept. of Electronic and Information Engineering,The Hong Kong Polytechnic University • Introduction • Motivation • In practical speaker verification, additive and convolutive noise cause mismatches between training and recognition conditions, degrading the performance. • Methods • A fusion system that combines a multi-condition PLDA model and a mixture of SNR-dependent PLDA models is proposed to make the verification system noise robust. • Key Findings • Results on NIST 2012 SRE show that (1) the SNR-dependent PLDA models can reduce EER, (2) the fusion system is more robust than the conventional i-vector/PLDA systems under noisy conditions, and (3) the SNR-dependent PLDA models are insensitive to Z-norm parameters. • Methods • Hard-Decision SNR-Dependent PLDA Results • Motivation of Methods • SNR Distribution in NIST 2012 SRE • Soft-Decision SNR-Dependent PLDA These histograms suggests that the test utterances exhibits a wide range of SNR. • Decision Weights System 1: Fusion of SNR-independent and hard-decision SNR-dependent PLDA System 2: Fusion of SNR-independent and soft-decision SNR-dependent PLDA System 3: Fusion of SNR-independent, hard- and soft-decision SNR-dependent PLDA

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