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Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification

Citation Author(s):
Jianbo Ma, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Kong Aik Lee
Submitted by:
Kong Aik Lee
Last updated:
18 April 2018 - 3:07am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Kong Aik Lee
Paper Code:
4426

Abstract 

Abstract: 

Phonetic variability is one of the primary challenges in short duration speaker verification. This paper proposes a novel method that modifies the standard normal distribution prior in the total variability model to use a mixture of Gaussians as the prior distribution. The proposed speaker-phonetic vectors are then estimated from the posterior probability of latent variables, and each vector has a phonetic meaning. Unlike the standard total variability model, the proposed method can incorporate a phoneme classifier to perform soft content matching, which has the potential to solve the phonetic variability problem. Parameter estimation and scoring formulae for speaker-phonetic vectors method are presented. Experimental results obtained using NIST 2010 data show that the proposed technique leads to relative improvements of more than 30% when fused with total variability model and tested on 3 second duration test files.

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Dataset Files

JIANBOMA_ICASSP_2018.pdf

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