1 citations · 1 across the 6 of their papers we have counts for
6 papers
FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning
Ju Yeon Kang, Ji Won Yoon, Semin Kim +2
Recently, fake audio detection has gained significant attention, as advancements in speech synthesis and voice conversion have increased the vulnerability of automatic speaker veri…
HILCodec: High-Fidelity and Lightweight Neural Audio Codec
Sunghwan Ahn, Beom Jun Woo, Min Hyun Han +2
The recent advancement of end-to-end neural audio codecs enables compressing audio at very low bitrates while reconstructing the output audio with high fidelity. Nonetheless, such…
EEND-DEMUX: End-to-End Neural Speaker Diarization via Demultiplexed Speaker Embeddings
Sung Hwan Mun, Min Hyun Han, Canyeong Moon +1
In recent years, there have been studies to further improve the end-to-end neural speaker diarization (EEND) systems. This letter proposes the EEND-DEMUX model, a novel framework u…
Towards single integrated spoofing-aware speaker verification embeddings
Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…
Fully Unsupervised Training of Few-shot Keyword Spotting
Dongjune Lee, Minchan Kim, Sung Hwan Mun +2
For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keyw…
Disentangled Speaker Representation Learning via Mutual Information Minimization
Sung Hwan Mun, Min Hyun Han, Minchan Kim +2
Domain mismatch problem caused by speaker-unrelated feature has been a major topic in speaker recognition. In this paper, we propose an explicit disentanglement framework to unrave…