no code implementations • 31 Aug 2023 • Kyuhong Shim, Jinkyu Lee, Simyung Chang, Kyuwoong Hwang
Streaming automatic speech recognition (ASR) models are restricted from accessing future context, which results in worse performance compared to the non-streaming models.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 28 Aug 2023 • Jin Bok Park, Jinkyu Lee, Muhyun Back, Hyunmin Han, David T. Ma, Sang Min Won, Sung Soo Hwang, Il Yong Chun
Vehicle control data is constructed by many hours of human driving, and it is challenging to construct large vehicle control datasets.
no code implementations • 19 Oct 2022 • Donghwa Kang, Seunghoon Lee, Hoon Sung Chwa, Seung-Hwan Bae, Chang Mook Kang, Jinkyu Lee, Hyeongboo Baek
Focusing on multiple choices of a workload pair of detection and association, which are two main components of the tracking-by-detection approach for MOT, we tailor a measure of object confidence for RT-MOT and develop how to estimate the measure for the next frame of each MOT task.
4 code implementations • 8 Jun 2021 • Byeonggeun Kim, Simyung Chang, Jinkyu Lee, Dooyong Sung
We present a broadcasted residual learning method to achieve high accuracy with small model size and computational load.
Ranked #2 on Keyword Spotting on Google Speech Commands
no code implementations • 30 Apr 2021 • Jinkyu Lee, Muhyun Back, Sung Soo Hwang, Il Yong Chun
Second, conventional monocular SLAM uses inappropriate mapping factors such as dynamic objects and low-parallax areas in mapping.
no code implementations • 1 Apr 2021 • Muhyun Back, Jinkyu Lee, Kyuho Bae, Sung Soo Hwang, Il Yong Chun
Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane.
no code implementations • 11 Oct 2019 • Byeonggeun Kim, Mingu Lee, Jinkyu Lee, Yeonseok Kim, Kyuwoong Hwang
A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 10 Oct 2019 • Mingu Lee, Jinkyu Lee, Hye Jin Jang, Byeonggeun Kim, Wonil Chang, Kyuwoong Hwang
Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models.