Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification

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Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification
Title:
Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification
Journal Title:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Keywords:
Publication Date:
05 May 2023
Citation:
Wang, Q., Lee, K. A., & Liu, T. (2023). Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp49357.2023.10097019
Abstract:
Speech utterances recorded under differing conditions exhibit varying degrees of confidence in their embedding estimates, i.e., uncertainty, even if they are extracted using the same neural network. This paper aims to incorporate the uncertainty estimate produced in the xi-vector network front-end with a probabilistic linear discriminant analysis (PLDA) back-end scoring for speaker verification. To achieve this we derive a posterior covariance matrix, which measures the uncertainty, from the frame-wise precisions to the embedding space. We propose a log-likelihood ratio function for the PLDA scoring with the uncertainty propagation. We also propose to replace the length normalization pre-processing technique with a length scaling technique for the application of uncertainty propagation in the back-end. Experimental results on the VoxCeleb-1, SITW test sets as well as a domain-mismatched CNCeleb1-E set show the effectiveness of the proposed techniques with 14.5%–41.3% EER reductions and 4.6%–25.3% minDCF reductions.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: SERC under the Council Research Fund (CRF)
Grant Reference no. : NA
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-7281-6327-7
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