research
∙
09/11/2023
Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search Approach
Large language models (LLMs) have shown great promise for capturing cont...
share
research
∙
03/30/2022
Multi-scale Speaker Diarization with Dynamic Scale Weighting
Speaker diarization systems are challenged by a trade-off between the te...
research
∙
10/19/2021
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Federated Learning is a fast growing area of ML where the training datas...
research
∙
01/24/2021
A Review of Speaker Diarization: Recent Advances with Deep Learning
Speaker diarization is a task to label audio or video recordings with cl...
research
∙
07/31/2020
Designing Neural Speaker Embeddings with Meta Learning
Neural speaker embeddings trained using classification objectives have d...
research
∙
07/19/2020
Meta-learning with Latent Space Clustering in Generative Adversarial Network for Speaker Diarization
The performance of most speaker diarization systems with x-vector embedd...
research
∙
03/05/2020
Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap
In this study, we propose a new spectral clustering framework that can a...
research
∙
10/24/2019
Speaker diarization using latent space clustering in generative adversarial network
In this work, we propose deep latent space clustering for speaker diariz...
research
∙
11/27/2018
Speaker Diarization With Lexical Information
This work presents a novel approach to leverage lexical information for ...
research
∙
05/28/2018
