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Found 12 papers, 2 papers with code
An end-to-end approach for the verification problem: learning the right distance
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder.
Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion Recognition
In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and intermodal relationships across the modalities.
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification
Furthermore, we are able to significantly boost verification performance by averaging our different GAN models at the score level, achieving a relative improvement of 7. 2% over the baseline.
Adapting End-to-End Neural Speaker Verification to New Languages and Recording Conditions with Adversarial Training
In this article we propose a novel approach for adapting speaker embeddings to new domains based on adversarial training of neural networks.
Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan
This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021.
Learning Semantic Similarities for Prototypical Classifiers
Recent metric learning approaches parametrize semantic similarity measures through the use of an encoder trained along with a similarity model, which operates over pairs of representations.
Robust Speech Representation Learning via Flow-based Embedding Regularization
Over the recent years, various deep learning-based methods were proposed for extracting a fixed-dimensional embedding vector from speech signals.
Attentive activation function for improving end-to-end spoofing countermeasure systems
The main objective of the spoofing countermeasure system is to detect the artifacts within the input speech caused by the speech synthesis or voice conversion process.
Audio-Visual Speaker Verification via Joint Cross-Attention
We have shown that efficiently leveraging the intra- and inter-modal relationships significantly improves the performance of audio-visual fusion for speaker verification.
Dynamic Cross Attention for Audio-Visual Person Verification
In this paper, we propose a Dynamic Cross-Attention (DCA) model that can dynamically select the cross-attended or unattended features on the fly based on the strong or weak complementary relationships, respectively, across audio and visual modalities.
Audio-Visual Person Verification based on Recursive Fusion of Joint Cross-Attention
In this paper, we have investigated the prospect of effectively capturing both the intra- and inter-modal relationships across audio and visual modalities, which can play a crucial role in significantly improving the fusion performance over unimodal systems.
Cross-Attention is Not Always Needed: Dynamic Cross-Attention for Audio-Visual Dimensional Emotion Recognition
We also compare the proposed approach with other variants of cross-attention and show that the proposed model consistently improves the performance on both datasets.
