no code implementations • ICML 2020 • Yu-Wen Chen, Antonio Orvieto, Aurelien Lucchi
Derivative-free optimization (DFO) has recently gained a lot of momentum in machine learning, spawning interest in the community to design faster methods for problems where gradients are not accessible.
1 code implementation • 21 Aug 2020 • Yu-Wen Chen, Kuo-Hsuan Hung, You-Jin Li, Alexander Chao-Fu Kang, Ya-Hsin Lai, Kai-Chun Liu, Szu-Wei Fu, Syu-Siang Wang, Yu Tsao
The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC), allowing CITISEN to be used as a platform for utilizing and evaluating SE models and flexibly extend the models to address various noise environments and users.
no code implementations • 3 Nov 2020 • Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang, Jonathan Sherman, Xugang Lu, Yu Tsao
Although deep learning algorithms are widely used for improving speech enhancement (SE) performance, the performance remains limited under highly challenging conditions, such as unseen noise or noise signals having low signal-to-noise ratios (SNRs).
no code implementations • 18 Dec 2020 • Yu-Wen Chen, Sourav Medya, Yi-Chun Chen
In this paper, we aim to identify and understand the impact of various factors on O3 formation and predict the O3 concentrations under different pollution-reduced and climate change scenarios.
no code implementations • 7 Feb 2021 • Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang, Jonathan Sherman, Wen-Chin Huang, Xugang Lu, Yu Tsao
Synthesized speech from articulatory movements can have real-world use for patients with vocal cord disorders, situations requiring silent speech, or in high-noise environments.
no code implementations • 7 Apr 2021 • Cheng-Hung Hu, Yi-Chiao Wu, Wen-Chin Huang, Yu-Huai Peng, Yu-Wen Chen, Pin-Jui Ku, Tomoki Toda, Yu Tsao, Hsin-Min Wang
The first track focuses on using a small number of 100 target utterances for voice cloning, while the second track focuses on using only 5 target utterances for voice cloning.
1 code implementation • 4 Nov 2021 • Yu-Wen Chen, Yu Tsao
Speech intelligibility and quality assessment models are essential tools for researchers to evaluate and improve speech processing models.
no code implementations • 9 Mar 2022 • Hsuan-Kung Yang, Tsu-Ching Hsiao, Ting-Hsuan Liao, Hsu-Shen Liu, Li-Yuan Tsao, Tzu-Wen Wang, Shan-Ya Yang, Yu-Wen Chen, Huang-Ru Liao, Chun-Yi Lee
In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks.
1 code implementation • 11 Dec 2022 • Yu-Wen Chen, Hsin-Min Wang, Yu Tsao
We converted the script into a speech corpus using two text-to-speech systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 24 Aug 2023 • Yu-Wen Chen, Zhou Yu, Julia Hirschberg
The design of automatic speech pronunciation assessment can be categorized into closed and open response scenarios, each with strengths and limitations.
no code implementations • 3 Sep 2023 • Yu-Wen Chen, Julia Hirschberg, Yu Tsao
Speech emotion recognition (SER) often experiences reduced performance due to background noise.
1 code implementation • 22 Sep 2023 • Ryandhimas E. Zezario, Yu-Wen Chen, Szu-Wei Fu, Yu Tsao, Hsin-Min Wang, Chiou-Shann Fuh
The first part of this study investigates the correlation between the embedding features of Whisper and two self-supervised learning (SSL) models with subjective quality and intelligibility scores.