11 accepted papers
Using clustering-driven annotations to train a neural network can be a tricky task because of label noise. In this paper, we propose a dynamic and adaptive label noise cleansing method, called AdaptiveDrop which combines both label noise filtering and correction simultaneously in cascade to combine…
Recently, Vision Transformers (ViTs) have shown remarkable success in various computer vision applications. In this work, we have explored the potential of ViTs, pre-trained on visual data, for audio-visual speaker verification. To cope with the challenges of large-scale training, we introduce the L…
In contrast to text-independent speaker verification, which has received significant attention from researchers and has many competitions dedicated to it, text-dependent speaker verification (TdSV) has been less explored recently. The TdSV Challenge 2024 was organized to analyze and explore novel me…
Clustering is an unsupervised learning technique, which leverages a large amount of unlabeled data to learn cluster-wise representations from speech. One of the most popular self-supervised techniques to train a speaker verification system is to predict the pseudo-labels using clustering algorithms…
Extraction of a speaker embedding vector plays an important role in deep learning-based speaker verification. In this contribution, to extract speaker discriminant utterance level embeddings, we propose a hybrid neural network that employs both cross- and self-module attention pooling mechanisms. Mo…
Over the recent years, various self-supervised contrastive embedding learning methods for deep speaker verification were proposed. The performance of the self-supervised contrastive learning framework highly depends on the data augmentation technique, but due to the sensitive nature of speaker infor…
As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previouslyproposed end-to-end detection frameworks have shown to be effective in spotti…
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn for the learned distance-like model’s output. We first show it approximates a likelihood ratio which can be used for hypo…
In this article we propose a novel approach for adapting speaker embeddings to new domains based on adversarial training of neural networks. We apply our embeddings to the task of text-independent speaker verification, a challenging, real-world problem in biometric security. We further the developme…
This article presents a novel approach for learning domain-invariant speaker embeddings using Generative Adversarial Networks. The main idea is to confuse a domain discriminator so that it cannot tell if embeddings are from the source or target domains. We train several GAN variants using our propos…
This paper introduces a new formulation of Joint Factor Analysis (JFA) for text-dependent speaker recognition based on left-to-right modeling with tied mixture HMMs. It accommodates many different ways of extracting multiple features to characterize speakers (features may or may not be HMM state-dep…