5 citations · 8 across the 5 of their papers we have counts for
5 papers
Deep User Identification Model with Multiple Biometrics
Hyoung-Kyu Song, Ebrahim AlAlkeem, Jaewoong Yun +5
Identification using biometrics is an important yet challenging task. Abundant research has been conducted on identifying personal identity or gender using given signals. Various t…
Adjusting Pleasure-Arousal-Dominance for Continuous Emotional Text-to-speech Synthesizer
Azam Rabiee, Tae-Ho Kim, Soo-Young Lee
Emotion is not limited to discrete categories of happy, sad, angry, fear, disgust, surprise, and so on. Instead, each emotion category is projected into a set of nearly independent…
Effective Network Compression Using Simulation-Guided Iterative Pruning
Dae-Woong Jeong, Jaehun Kim, Youngseok Kim +2
Existing high-performance deep learning models require very intensive computing. For this reason, it is difficult to embed a deep learning model into a system with limited resource…
End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights
Myungsu Chae, Tae-Ho Kim, Young Hoon Shin +2
Multi-task learning is a method for improving the generalizability of multiple tasks. In order to perform multiple classification tasks with one neural network model, the losses of…
A Fully Time-domain Neural Model for Subband-based Speech Synthesizer
Azam Rabiee, Geonmin Kim, Tae-Ho Kim +1
This paper introduces a deep neural network model for subband-based speech synthesizer. The model benefits from the short bandwidth of the subband signals to reduce the complexity…