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Found 11 papers, 5 papers with code
Discriminative Training of VBx Diarization
Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges.
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods.
Toroidal Probabilistic Spherical Discriminant Analysis
It extends PSDA with the ability to model within and between-speaker variabilities in toroidal submanifolds of the hypersphere.
Training Speaker Embedding Extractors Using Multi-Speaker Audio with Unknown Speaker Boundaries
In this paper, we demonstrate a method for training speaker embedding extractors using weak annotation.
Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings
In speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring backends are commonly used, namely cosine scoring or PLDA.
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.
Probabilistic embeddings for speaker diarization
We apply the proposed probabilistic embeddings as input to an agglomerative hierarchical clustering (AHC) algorithm to do diarization in the DIHARD'19 evaluation set.
BUT System Description to VoxCeleb Speaker Recognition Challenge 2019
The last two networks are one-dimensional CNN and are based on the x-vector extraction topology.
BUT VOiCES 2019 System Description
This is a description of our effort in VOiCES 2019 Speaker Recognition challenge.
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.
