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Found 12 papers, 2 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.
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.
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.
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.
