Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Heo, Hee Soo"'
This work presents a framework based on feature disentanglement to learn speaker embeddings that are robust to environmental variations. Our framework utilises an auto-encoder as a disentangler, dividing the input speaker embedding into components re
Externí odkaz:
http://arxiv.org/abs/2406.14559
Autor:
Heo, Hee-Soo, Nam, KiHyun, Lee, Bong-Jin, Kwon, Youngki, Lee, Minjae, Kim, You Jin, Chung, Joon Son
In the field of speaker verification, session or channel variability poses a significant challenge. While many contemporary methods aim to disentangle session information from speaker embeddings, we introduce a novel approach using an additional embe
Externí odkaz:
http://arxiv.org/abs/2309.14741
Autor:
Jung, Jee-weon, Seo, Soonshin, Heo, Hee-Soo, Kim, Geonmin, Kim, You Jin, Kwon, Young-ki, Lee, Minjae, Lee, Bong-Jin
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 audio inputs only and have shown limited performance. Recently, multi
Externí odkaz:
http://arxiv.org/abs/2306.00680
Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated in real-time are irreversible, and a few misjudgements in the early ph
Externí odkaz:
http://arxiv.org/abs/2211.04768
Speaker embedding extractors significantly influence the performance of clustering-based speaker diarisation systems. Conventionally, only one embedding is extracted from each speech segment. However, because of the sliding window approach, a segment
Externí odkaz:
http://arxiv.org/abs/2211.04060
The goal of this paper is to learn robust speaker representation for bilingual speaking scenario. The majority of the world's population speak at least two languages; however, most speaker recognition systems fail to recognise the same speaker when s
Externí odkaz:
http://arxiv.org/abs/2211.00437
Autor:
Jung, Jee-weon, Heo, Hee-Soo, Lee, Bong-Jin, Huh, Jaesung, Brown, Andrew, Kwon, Youngki, Watanabe, Shinji, Chung, Joon Son
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 challenges when adopting EEs for diarisation, from which we tackle two ke
Externí odkaz:
http://arxiv.org/abs/2210.14682
Autor:
Jung, Jee-weon, Heo, Hee-Soo, Lee, Bong-Jin, Lee, Jaesong, Shim, Hye-jin, Kwon, Youngki, Chung, Joon Son, Watanabe, Shinji
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 such a model. First, adequate architecture would be required. We explore
Externí odkaz:
http://arxiv.org/abs/2210.10985
Autor:
Shim, Hye-jin, Tak, Hemlata, Liu, Xuechen, Heo, Hee-Soo, Jung, Jee-weon, Chung, Joon Son, Chung, Soo-Whan, Yu, Ha-Jin, Lee, Bong-Jin, Todisco, Massimiliano, Delgado, Héctor, Lee, Kong Aik, Sahidullah, Md, Kinnunen, Tomi, Evans, Nicholas
Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM sol
Externí odkaz:
http://arxiv.org/abs/2204.09976
Autor:
Jung, Jee-weon, Tak, Hemlata, Shim, Hye-jin, Heo, Hee-Soo, Lee, Bong-Jin, Chung, Soo-Whan, Yu, Ha-Jin, Evans, Nicholas, Kinnunen, Tomi
The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scenario by introducing spoofed trials to the usual set of target and impo
Externí odkaz:
http://arxiv.org/abs/2203.14732