1 code implementation • 20 Jul 2022 • WonJun Moon, Ji-Hwan Kim, Jae-Pil Heo
Our exhaustive experiments validate the merits of LoRot as a pretext task tailored for supervised learning in terms of robustness and generalization capability.
Ranked #9 on Data Augmentation on ImageNet
no code implementations • 27 Jul 2020 • Soonshin Seo, Ji-Hwan Kim
Therefore, we propose a self-attentive multi-layer aggregation with feature recalibration and normalization for end-to-end speaker verification system.
no code implementations • 28 Jan 2020 • Soonshin Seo, Ji-Hwan Kim
Based on multi-layer aggregation, the output features of each residual layer are used for the MCSAE.
no code implementations • 21 Jun 2019 • Minkyu Lim, Ji-Hwan Kim
By contrast, a general-purpose deep learning framework, such as TensorFlow, can easily build various types of neural network architectures using a tensor-based computation method, but it is difficult to apply them to WFST-based speech recognition.
no code implementations • 11 Jul 2018 • Hosung Park, Dong-Hyun Lee, Minkyu Lim, Yoseb Kang, Juneseok Oh, Ji-Hwan Kim
In this paper, a time delay neural network (TDNN) based acoustic model is proposed to implement a fast-converged acoustic modeling for Korean speech recognition.