A Speaker Verification Backend For Improved Calibration Performance Across Varying Conditions

Abstract

In a recent work, we presented a discriminative backend for speaker verification that achieved good out-of-the-box calibration performance on most tested conditions containing varying levels of mismatch to the training conditions. This backend mimics the standard PLDA-based backend process used in most current speaker verification systems, including the calibration stage. All parameters of the backend are jointly trained to optimize the binary cross-entropy for the speaker verification task. Calibration robustness is achieved by making the parameters of the calibration stage a function of vectors representing the conditions of the signal, which are extracted using a model trained to predict condition labels. In this work, we propose a simplified version of this backend where the vectors used to compute the calibration parameters are estimated within the backend, without the need for a condition prediction model. We show that this simplified method provides similar performance to the pr

Stats

• citations7
• S2 citations—
• github stars0
• HF likes0
• heat score6.77
• arxiv keyferrer2020a

Related papers

• A Discriminative Condition-aware Backend For Speaker Verification (2019)6.34
• Multiobjective Optimization Training Of PLDA For Speaker Verification (2018)2.26
• Analyzing Speaker Verification Embedding Extractors And Back-ends Under Language And Channel Mismatch (2022)0.00
• Attention Back-end For Automatic Speaker Verification With Multiple Enrollment Utterances (2021)10.21
• Linear Regression For Speaker Verification (2018)0.00
• Joint Speaker Encoder And Neural Back-end Model For Fully End-to-end Automatic Speaker Verification With Multiple Enrollment Utterances (2022)0.00
• An Attention-based Backend Allowing Efficient Fine-tuning Of Transformer Models For Speaker Verification (2022)11.08
• Parameterized Channel Normalization For Far-field Deep Speaker Verification (2021)3.58