2 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
Pushing the Limits of End-to-End Diarization
Samuel J. Broughton, Lahiru Samarakoon
In this paper, we present state-of-the-art diarization error rates (DERs) on multiple publicly available datasets, including AliMeeting-far, AliMeeting-near, AMI-Mix, AMI-SDM, DIHA…
EEND-M2F: Masked-attention mask transformers for speaker diarization
Marc Härkönen, Samuel J. Broughton, Lahiru Samarakoon
In this paper, we make the explicit connection between image segmentation methods and end-to-end diarization methods. From these insights, we propose a novel, fully end-to-end diar…
Robust End-to-End Diarization with Domain Adaptive Training and Multi-Task Learning
Ivan Fung, Lahiru Samarakoon, Samuel J. Broughton
Due to the scarcity of publicly available diarization data, the model performance can be improved by training a single model with data from different domains. In this work, we prop…
Transformer Attractors for Robust and Efficient End-to-End Neural Diarization
Lahiru Samarakoon, Samuel J. Broughton, Marc Härkönen +1
End-to-end neural diarization with encoder-decoder based attractors (EEND-EDA) is a method to perform diarization in a single neural network. EDA handles the diarization of a flexi…
Improving End-to-End Neural Diarization Using Conversational Summary Representations
Samuel J. Broughton, Lahiru Samarakoon
Speaker diarization is a task concerned with partitioning an audio recording by speaker identity. End-to-end neural diarization with encoder-decoder based attractor calculation (EE…
Investigating Deep Neural Structures and their Interpretability in the Domain of Voice Conversion
Samuel J. Broughton, Md Asif Jalal, Roger K. Moore
Generative Adversarial Networks (GANs) are machine learning networks based around creating synthetic data. Voice Conversion (VC) is a subset of voice translation that involves tran…