2.3k citations · 7.4k across the 34 of their papers we have counts for
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VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge
Arsha Nagrani, Joon Son Chung, Jaesung Huh +6
We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020. The goal of this challenge was to assess how well current speaker…
Supervised attention for speaker recognition
Seong Min Kye, Joon Son Chung, Hoirin Kim
The recently proposed self-attentive pooling (SAP) has shown good performance in several speaker recognition systems. In SAP systems, the context vector is trained end-to-end toget…
Look who's not talking
Youngki Kwon, Hee Soo Heo, Jaesung Huh +2
The objective of this work is speaker diarisation of speech recordings 'in the wild'. The ability to determine speech segments is a crucial part of diarisation systems, accounting…
The ins and outs of speaker recognition: lessons from VoxSRC 2020
Yoohwan Kwon, Hee-Soo Heo, Bong-Jin Lee +1
The VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech 2020 offers a challenging evaluation for speaker recognition systems, which includes celebrities playing differen…
Playing a Part: Speaker Verification at the Movies
Andrew Brown, Jaesung Huh, Arsha Nagrani +2
The goal of this work is to investigate the performance of popular speaker recognition models on speech segments from movies, where often actors intentionally disguise their voice…
Graph Attention Networks for Speaker Verification
Jee-weon Jung, Hee-Soo Heo, Ha-Jin Yu +1
This work presents a novel back-end framework for speaker verification using graph attention networks. Segment-wise speaker embeddings extracted from multiple crops within an utter…
Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020
Hee Soo Heo, Bong-Jin Lee, Jaesung Huh +1
This report describes our submission to the VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech 2020. We perform a careful analysis of speaker recognition models based o…
Self-Supervised Learning of Audio-Visual Objects from Video
Triantafyllos Afouras, Andrew Owens, Joon Son Chung +1
Our objective is to transform a video into a set of discrete audio-visual objects using self-supervised learning. To this end, we introduce a model that uses attention to localize…
Cross attentive pooling for speaker verification
Seong Min Kye, Yoohwan Kwon, Joon Son Chung
The goal of this paper is text-independent speaker verification where utterances come from 'in the wild' videos and may contain irrelevant signal. While speaker verification is nat…
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues
Samuel Albanie, Gül Varol, Liliane Momeni +4
Recent progress in fine-grained gesture and action classification, and machine translation, point to the possibility of automated sign language recognition becoming a reality. A ke…
Augmentation adversarial training for self-supervised speaker recognition
Jaesung Huh, Hee Soo Heo, Jingu Kang +2
The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning i…
Spot the conversation: speaker diarisation in the wild
Joon Son Chung, Jaesung Huh, Arsha Nagrani +2
The goal of this paper is speaker diarisation of videos collected 'in the wild'. We make three key contributions. First, we propose an automatic audio-visual diarisation method for…
Metric Learning for Keyword Spotting
Jaesung Huh, Minjae Lee, Heesoo Heo +2
The goal of this work is to train effective representations for keyword spotting via metric learning. Most existing works address keyword spotting as a closed-set classification pr…
FaceFilter: Audio-visual speech separation using still images
Soo-Whan Chung, Soyeon Choe, Joon Son Chung +1
The objective of this paper is to separate a target speaker's speech from a mixture of two speakers using a deep audio-visual speech separation network. Unlike previous works that…
Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision
Soo-Whan Chung, Hong Goo Kang, Joon Son Chung
The goal of this work is to train discriminative cross-modal embeddings without access to manually annotated data. Recent advances in self-supervised learning have shown that effec…
In defence of metric learning for speaker recognition
Joon Son Chung, Jaesung Huh, Seongkyu Mun +7
The objective of this paper is 'open-set' speaker recognition of unseen speakers, where ideal embeddings should be able to condense information into a compact utterance-level repre…
Disentangled Speech Embeddings using Cross-modal Self-supervision
Arsha Nagrani, Joon Son Chung, Samuel Albanie +1
The objective of this paper is to learn representations of speaker identity without access to manually annotated data. To do so, we develop a self-supervised learning objective tha…