Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Kim Ju-Ho"'
Autor:
Kim, Seung-bin, Lim, Chan-yeong, Heo, Jungwoo, Kim, Ju-ho, Shin, Hyun-seo, Koo, Kyo-Won, Yu, Ha-Jin
In speaker verification systems, the utilization of short utterances presents a persistent challenge, leading to performance degradation primarily due to insufficient phonetic information to characterize the speakers. To overcome this obstacle, we pr
Externí odkaz:
http://arxiv.org/abs/2406.07103
Audio deepfake detection (ADD) is the task of detecting spoofing attacks generated by text-to-speech or voice conversion systems. Spoofing evidence, which helps to distinguish between spoofed and bona-fide utterances, might exist either locally or gl
Externí odkaz:
http://arxiv.org/abs/2309.08208
Background noise considerably reduces the accuracy and reliability of speaker verification (SV) systems. These challenges can be addressed using a speech enhancement system as a front-end module. Recently, diffusion probabilistic models (DPMs) have e
Externí odkaz:
http://arxiv.org/abs/2309.08320
Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been commonly used. In
Externí odkaz:
http://arxiv.org/abs/2307.10628
The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in employing them, which makes development and deployment difficult. Sever
Externí odkaz:
http://arxiv.org/abs/2305.17394
In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able to analyze these features. In this research, we propose a novel framew
Externí odkaz:
http://arxiv.org/abs/2211.01599
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability h
Externí odkaz:
http://arxiv.org/abs/2211.02227
Publikováno v:
Proc. Interspeech 2022
The use of deep neural networks (DNN) has dramatically elevated the performance of automatic speaker verification (ASV) over the last decade. However, ASV systems can be easily neutralized by spoofing attacks. Therefore, the Spoofing-Aware Speaker Ve
Externí odkaz:
http://arxiv.org/abs/2206.13807
Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility. Various studies have used separate pretrained enhancement models as the front-end module o
Externí odkaz:
http://arxiv.org/abs/2206.13044
Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of systems. To deal with this issue, we propose a speaker verification system
Externí odkaz:
http://arxiv.org/abs/2112.07935