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Found 24 papers, 5 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.
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.
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.
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.
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.
Adapting Speaker Embeddings for Speaker Diarisation
The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation.
Graph Attention Networks for Speaker Verification
The proposed framework inputs segment-wise speaker embeddings from an enrollment and a test utterance and directly outputs a similarity score.
A study on the role of subsidiary information in replay attack spoofing detection
For addition, we utilize the multi-task learning framework to include subsidiary information to the code.
Self-supervised pre-training with acoustic configurations for replay spoofing detection
Constructing a dataset for replay spoofing detection requires a physical process of playing an utterance and re-recording it, presenting a challenge to the collection of large-scale datasets.
Cosine similarity-based adversarial process
In particular, the adversarial process degrades the performance of the subsidiary model by eliminating the subsidiary information in the input which, in assumption, may degrade the performance of the primary model.
Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge
To detect unrevealed characteristics that reside in a replayed speech, we directly input spectrograms into an end-to-end DNN without knowledge-based intervention.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification.
End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification
Each speaker basis is designed to represent the corresponding speaker in the process of training deep neural networks.
Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings
The short duration of an input utterance is one of the most critical threats that degrade the performance of speaker verification systems.
Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes
We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification.
