Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Mun, Sung Hwan"'
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 utilizing demultiplexed speaker embeddings. In this work, we focus on d
Externí odkaz:
http://arxiv.org/abs/2312.06065
Autor:
Mun, Sung Hwan, Shim, Hye-jin, Tak, Hemlata, Wang, Xin, Liu, Xuechen, Sahidullah, Md, Jeong, Myeonghun, Han, Min Hyun, Todisco, Massimiliano, Lee, Kong Aik, Yamagishi, Junichi, Evans, Nicholas, Kinnunen, Tomi, Kim, Nam Soo, Jung, Jee-weon
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 as target speakers' spoofed inputs should be addressed. Second, competi
Externí odkaz:
http://arxiv.org/abs/2305.19051
Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios with a set of pre-defined speakers. However, synthesizing a new speaker's
Externí odkaz:
http://arxiv.org/abs/2210.05979
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 keywords with only a few enrollment samples. To alleviate the expensive da
Externí odkaz:
http://arxiv.org/abs/2210.02732
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 unravel speaker-relevant features from speaker-unrelated features via mutual
Externí odkaz:
http://arxiv.org/abs/2208.08012
The majority of recent state-of-the-art speaker verification architectures adopt multi-scale processing and frequency-channel attention mechanisms. Convolutional layers of these models typically have a fixed kernel size, e.g., 3 or 5. In this study,
Externí odkaz:
http://arxiv.org/abs/2204.01005
In this paper, we propose self-supervised speaker representation learning strategies, which comprise of a bootstrap equilibrium speaker representation learning in the front-end and an uncertainty-aware probabilistic speaker embedding training in the
Externí odkaz:
http://arxiv.org/abs/2112.08929
In this paper, we propose a simple but powerful unsupervised learning method for speaker recognition, namely Contrastive Equilibrium Learning (CEL), which increases the uncertainty on nuisance factors latent in the embeddings by employing the uniform
Externí odkaz:
http://arxiv.org/abs/2010.11433
This paper describes our submission to Task 1 of the Short-duration Speaker Verification (SdSV) challenge 2020. Task 1 is a text-dependent speaker verification task, where both the speaker and phrase are required to be verified. The submitted systems
Externí odkaz:
http://arxiv.org/abs/2010.11408
Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the classical embedding techniques, the deep learning-based methods are know
Externí odkaz:
http://arxiv.org/abs/2008.03024