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Found 9 papers, 4 papers with code
Improving Speaker Verification with Self-Pretrained Transformer Models
Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest.
Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters
Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks.
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.
MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification
Motivated by unconsolidated data situation and the lack of a standard benchmark in the field, we complement our previous efforts and present a comprehensive corpus designed for training and evaluating text-independent multi-channel speaker verification systems.
BUT System Description for DIHARD Speech Diarization Challenge 2019
This paper describes the systems developed by the BUT team for the four tracks of the second DIHARD speech diarization challenge.
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.
Learning document embeddings along with their uncertainties
We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its co-variance.
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BUT VOiCES 2019 System Description
This is a description of our effort in VOiCES 2019 Speaker Recognition challenge.
