no code implementations • NIDCP (LREC) 2022 • John H.L. Hansen, Aditya Joglekar, Szu-Jui Chen, Meena Chandra Shekar, Chelzy Belitz
We aim to make this entire resource and supporting speech technology meta-data creation publicly available as a Community Resource for the development of speech and behavioral science.
no code implementations • 20 Jan 2023 • Szu-Jui Chen, Debjyoti Paul, Yutong Pang, Peng Su, Xuedong Zhang
With the emergence of automatic speech recognition (ASR) models, converting the spoken form text (from ASR) to the written form is in urgent need.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 30 Jun 2022 • Szu-Jui Chen, Jiamin Xie, John H. L. Hansen
As such, we further propose a feature refinement loss for decorrelation to efficiently combine the set of input features.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 23 Sep 2021 • Szu-Jui Chen, Wei Xia, John H. L. Hansen
With additional techniques such as pronunciation and silence probability modeling, plus multi-style training, we achieve a +5. 42% and +3. 18% relative WER improvement for the development and evaluation sets of the Fearless Steps Corpus.
no code implementations • 27 Mar 2018 • Szu-Jui Chen, Aswin Shanmugam Subramanian, Hainan Xu, Shinji Watanabe
This paper describes a new baseline system for automatic speech recognition (ASR) in the CHiME-4 challenge to promote the development of noisy ASR in speech processing communities by providing 1) state-of-the-art system with a simplified single system comparable to the complicated top systems in the challenge, 2) publicly available and reproducible recipe through the main repository in the Kaldi speech recognition toolkit.
Ranked #2 on Noisy Speech Recognition on CHiME real
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5