399 citations · 538 across the 20 of their papers we have counts for
10 papers · 1 filter
Encoder-decoder multimodal speaker change detection
Jee-weon Jung, Soonshin Seo, Hee-Soo Heo +5
The task of speaker change detection (SCD), which detects points where speakers change in an input, is essential for several applications. Several studies solved the SCD task using…
High-resolution embedding extractor for speaker diarisation
Hee-Soo Heo, Youngki Kwon, Bong-Jin Lee +2
Speaker embedding extractors significantly influence the performance of clustering-based speaker diarisation systems. Conventionally, only one embedding is extracted from each spee…
Large-scale learning of generalised representations for speaker recognition
Jee-weon Jung, Hee-Soo Heo, Bong-Jin Lee +5
The objective of this work is to develop a speaker recognition model to be used in diverse scenarios. We hypothesise that two components should be adequately configured to build su…
In search of strong embedding extractors for speaker diarisation
Jee-weon Jung, Hee-Soo Heo, Bong-Jin Lee +5
Speaker embedding extractors (EEs), which map input audio to a speaker discriminant latent space, are of paramount importance in speaker diarisation. However, there are several cha…
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
Hye-jin Shim, Hemlata Tak, Xuechen Liu +12
Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent…
SASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan
Jee-weon Jung, Hemlata Tak, Hye-jin Shim +7
ASV (automatic speaker verification) systems are intrinsically required to reject both non-target (e.g., voice uttered by different speaker) and spoofed (e.g., synthesised or conve…
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity
You Jin Kim, Hee-Soo Heo, Jee-weon Jung +3
The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation. Speaker embeddings play a crucial role in the performance of diarisation sys…
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…
Who said that?: Audio-visual speaker diarisation of real-world meetings
Joon Son Chung, Bong-Jin Lee, Icksang Han
The goal of this work is to determine 'who spoke when' in real-world meetings. The method takes surround-view video and single or multi-channel audio as inputs, and generates robus…