Search Results for author: Yen-Ju Lu

Found 10 papers, 3 papers with code

ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding

1 code implementation19 Jul 2022 Yen-Ju Lu, Xuankai Chang, Chenda Li, Wangyou Zhang, Samuele Cornell, Zhaoheng Ni, Yoshiki Masuyama, Brian Yan, Robin Scheibler, Zhong-Qiu Wang, Yu Tsao, Yanmin Qian, Shinji Watanabe

To showcase such integration, we performed experiments on carefully designed synthetic datasets for noisy-reverberant multi-channel ST and SLU tasks, which can be used as benchmark corpora for future research.

Automatic Speech Recognition Robust Speech Recognition +5

Conditional Diffusion Probabilistic Model for Speech Enhancement

1 code implementation10 Feb 2022 Yen-Ju Lu, Zhong-Qiu Wang, Shinji Watanabe, Alexander Richard, Cheng Yu, Yu Tsao

Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs.

Speech Enhancement Speech Synthesis

Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem

no code implementations17 Dec 2021 Jing Shi, Xuankai Chang, Tomoki Hayashi, Yen-Ju Lu, Shinji Watanabe, Bo Xu

Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols, and convert the paradigm of the speech separation/enhancement related tasks from regression to classification.

Speech Separation

A Study on Speech Enhancement Based on Diffusion Probabilistic Model

1 code implementation25 Jul 2021 Yen-Ju Lu, Yu Tsao, Shinji Watanabe

Based on this property, we propose a diffusion probabilistic model-based speech enhancement (DiffuSE) model that aims to recover clean speech signals from noisy signals.

Speech Enhancement

Speech Enhancement Guided by Contextual Articulatory Information

no code implementations15 Nov 2020 Yen-Ju Lu, Chia-Yu Chang, Cheng Yu, Ching-Feng Liu, Jeih-weih Hung, Shinji Watanabe, Yu Tsao

Previous studies have confirmed that by augmenting acoustic features with the place/manner of articulatory features, the speech enhancement (SE) process can be guided to consider the articulatory properties of the input speech when performing enhancement to attain performance improvements.

Automatic Speech Recognition Denoising +4

Incorporating Broad Phonetic Information for Speech Enhancement

no code implementations13 Aug 2020 Yen-Ju Lu, Chien-Feng Liao, Xugang Lu, Jeih-weih Hung, Yu Tsao

In noisy conditions, knowing speech contents facilitates listeners to more effectively suppress background noise components and to retrieve pure speech signals.

Denoising Speech Enhancement

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