Search Results for author: Yu-Wen Chen

Found 8 papers, 2 papers with code

Investigation of Factorized Optical Flows as Mid-Level Representations

no code implementations9 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.

Optical Flow Estimation reinforcement-learning

InQSS: a speech intelligibility and quality assessment model using a multi-task learning network

1 code implementation4 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.

Multi-Task Learning

The AS-NU System for the M2VoC Challenge

no code implementations7 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.

Voice Cloning

EMA2S: An End-to-End Multimodal Articulatory-to-Speech System

no code implementations7 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.

Investigating Ground-level Ozone Formation: A Case Study in Taiwan

no code implementations18 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.

A Study of Incorporating Articulatory Movement Information in Speech Enhancement

no code implementations3 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).

Speech Enhancement

CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application

1 code implementation21 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.

Acoustic Scene Classification Data Augmentation +2

An Accelerated DFO Algorithm for Finite-sum Convex Functions

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.

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