A Speaker Verification Backend for Improved Calibration Performance across Varying Conditions

• URL: http://arxiv.org/abs/2002.03802v1
• Date: Wed, 5 Feb 2020 15:37:46 GMT
• Title: A Speaker Verification Backend for Improved Calibration Performance across Varying Conditions
• Authors: Luciana Ferrer and Mitchell McLaren
• Abstract summary: We present a discriminative backend for speaker verification that achieved good out-of-the-box calibration performance. All parameters of the backend are jointly trained to optimize the binary cross-entropy for the speaker verification task. We show that this simplified method provides similar performance to the previously proposed method while being simpler to implement, and having less requirements on the training data.
• Score: 21.452221762153577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 previously proposed method while being simpler to implement, and having less requirements on the training data. Further, we provide an analysis of different aspects of the method including the effect of initialization, the nature of the vectors used to compute the calibration parameters, and the effect that the random seed and the number of training epochs has on performance. We also compare the proposed method with the trial-based calibration (TBC) method that, to our knowledge, was the state-of-the-art for achieving good calibration across varying conditions. We show that the proposed method outperforms TBC while also being several orders of magnitude faster to run, comparable to the standard PLDA baseline.

Related papers

• Optimizing Estimators of Squared Calibration Errors in Classification [2.3020018305241337]
  We propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors. Our approach advocates for a training-validation-testing pipeline when estimating a calibration error.
  arXiv  Detail & Related papers  (2024-10-09T15:58:06Z)
• C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion [54.81141583427542]
  In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. This paper explores calibration during test-time prompt tuning by leveraging the inherent properties of CLIP. We present a novel method, Calibrated Test-time Prompt Tuning (C-TPT), for optimizing prompts during test-time with enhanced calibration.
  arXiv  Detail & Related papers  (2024-03-21T04:08:29Z)
• Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [56.629245030893685]
  We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
  arXiv  Detail & Related papers  (2023-10-31T06:19:40Z)
• Sharp Calibrated Gaussian Processes [58.94710279601622]
  State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance. We present a calibration approach that generates predictive quantiles using a computation inspired by the vanilla Gaussian process posterior variance. Our approach is shown to yield a calibrated model under reasonable assumptions.
  arXiv  Detail & Related papers  (2023-02-23T12:17:36Z)
• Modular Conformal Calibration [80.33410096908872]
  We introduce a versatile class of algorithms for recalibration in regression. This framework allows one to transform any regression model into a calibrated probabilistic model. We conduct an empirical study of MCC on 17 regression datasets.
  arXiv  Detail & Related papers  (2022-06-23T03:25:23Z)
• Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration [57.568461777747515]
  We introduce a novel calibration method, Parametrized Temperature Scaling (PTS) We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
  arXiv  Detail & Related papers  (2021-02-24T10:18:30Z)
• A Speaker Verification Backend with Robust Performance across Conditions [28.64769660252556]
  A standard method for speaker verification consists of extracting speaker embeddings with a deep neural network. This method is known to result in systems that work poorly on conditions different from those used to train the calibration model. We propose to modify the standard backend, introducing an adaptive calibrator that uses duration and other automatically extracted side-information to adapt to the conditions of the inputs.
  arXiv  Detail & Related papers  (2021-02-02T21:27:52Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• Calibrating Structured Output Predictors for Natural Language Processing [8.361023354729731]
  We propose a general calibration scheme for output entities of interest in neural-network based structured prediction models. Our proposed method can be used with any binary class calibration scheme and a neural network model. We show that our method outperforms current calibration techniques for named-entity-recognition, part-of-speech and question answering.
  arXiv  Detail & Related papers  (2020-04-09T04:14:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.