Search Results for author:
Found 17 papers, 3 papers with code
Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification
In the field of speaker verification, session or channel variability poses a significant challenge.
Encoder-decoder multimodal speaker change detection
The task of speaker change detection (SCD), which detects points where speakers change in an input, is essential for several applications.
Absolute decision corrupts absolutely: conservative online speaker diarisation
Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains.
High-resolution embedding extractor for speaker diarisation
Extracted dense frame-level embeddings can each represent a speaker.
In search of strong embedding extractors for speaker diarisation
First, the evaluation is not straightforward because the features required for better performance differ between speaker verification and diarisation.
Large-scale learning of generalised representations for speaker recognition
We also show that training with proposed large data configurations gives better performance.
Curriculum learning for self-supervised speaker verification
The goal of this paper is to train effective self-supervised speaker representations without identity labels.
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions.
Pushing the limits of raw waveform speaker recognition
Our best model achieves an equal error rate of 0. 89%, which is competitive with the state-of-the-art models based on handcrafted features, and outperforms the best model based on raw waveform inputs by a large margin.
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity
The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation.
Multi-scale speaker embedding-based graph attention networks for speaker diarisation
The objective of this work is effective speaker diarisation using multi-scale speaker embeddings.
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains.
Ranked #1 on
Voice Anti-spoofing
on ASVspoof 2019 - LA
Look Who's Talking: Active Speaker Detection in the Wild
Face tracks are extracted from the videos and active segments are annotated based on the timestamps of VoxConverse in a semi-automatic way.
Adapting Speaker Embeddings for Speaker Diarisation
The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation.
Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network
In this work, we propose an overlapped speech detection system trained as a three-class classifier.
End-to-End Lip Synchronisation Based on Pattern Classification
The goal of this work is to synchronise audio and video of a talking face using deep neural network models.
Who said that?: Audio-visual speaker diarisation of real-world meetings
The goal of this work is to determine 'who spoke when' in real-world meetings.
