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Found 8 papers, 2 papers with code
Analyzing speaker verification embedding extractors and back-ends under language and channel mismatch
In this paper, we analyze the behavior and performance of speaker embeddings and the back-end scoring model under domain and language mismatch.
A Speaker Verification Backend with Robust Performance across Conditions
When trained on a number of diverse datasets that are labeled only with respect to speaker, the proposed backend consistently and, in some cases, dramatically improves calibration, compared to the standard PLDA approach, on a number of held-out datasets, some of which are markedly different from the training data.
Large-Scale Speaker Diarization of Radio Broadcast Archives
This paper describes our initial efforts to build a large-scale speaker diarization (SD) and identification system on a recently digitized radio broadcast archive from the Netherlands which has more than 6500 audio tapes with 3000 hours of Frisian-Dutch speech recorded between 1950-2016.
Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors
We have recently introduced a fast scoring algorithm for a discriminatively trained HT-PLDA backend.
Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA model
Embeddings in machine learning are low-dimensional representations of complex input patterns, with the property that simple geometric operations like Euclidean distances and dot products can be used for classification and comparison tasks.
A Generative Model for Score Normalization in Speaker Recognition
We propose a theoretical framework for thinking about score normalization, which confirms that normalization is not needed under (admittedly fragile) ideal conditions.
Generative, Fully Bayesian, Gaussian, Openset Pattern Classifier
This report works out the details of a closed-form, fully Bayesian, multiclass, openset, generative pattern classifier using multivariate Gaussian likelihoods, with conjugate priors.
The PAV algorithm optimizes binary proper scoring rules
There has been much recent interest in application of the pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the probabilistic outputs of automatic pattern recognition and machine learning algorithms.
