Search Results for author: Tsun-An Hsieh

Found 7 papers, 3 papers with code

OSSEM: one-shot speaker adaptive speech enhancement using meta learning

no code implementations10 Nov 2021 Cheng Yu, Szu-Wei Fu, Tsun-An Hsieh, Yu Tsao, Mirco Ravanelli

Although deep learning (DL) has achieved notable progress in speech enhancement (SE), further research is still required for a DL-based SE system to adapt effectively and efficiently to particular speakers.

Meta-Learning Speech Enhancement

Mutual Information Continuity-constrained Estimator

no code implementations29 Sep 2021 Tsun-An Hsieh, Cheng Yu, Ying Hung, Chung-Ching Lin, Yu Tsao

Accordingly, we propose Mutual Information Continuity-constrained Estimator (MICE).

Density Estimation

Speech Recovery for Real-World Self-powered Intermittent Devices

no code implementations9 Jun 2021 Yu-Chen Lin, Tsun-An Hsieh, Kuo-Hsuan Hung, Cheng Yu, Harinath Garudadri, Yu Tsao, Tei-Wei Kuo

The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications.

MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement

2 code implementations8 Apr 2021 Szu-Wei Fu, Cheng Yu, Tsun-An Hsieh, Peter Plantinga, Mirco Ravanelli, Xugang Lu, Yu Tsao

The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory.

Speech Enhancement

Improving Perceptual Quality by Phone-Fortified Perceptual Loss using Wasserstein Distance for Speech Enhancement

1 code implementation28 Oct 2020 Tsun-An Hsieh, Cheng Yu, Szu-Wei Fu, Xugang Lu, Yu Tsao

Speech enhancement (SE) aims to improve speech quality and intelligibility, which are both related to a smooth transition in speech segments that may carry linguistic information, e. g. phones and syllables.

Speech Enhancement

Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing

no code implementations18 Jun 2020 Szu-Wei Fu, Chien-Feng Liao, Tsun-An Hsieh, Kuo-Hsuan Hung, Syu-Siang Wang, Cheng Yu, Heng-Cheng Kuo, Ryandhimas E. Zezario, You-Jin Li, Shang-Yi Chuang, Yen-Ju Lu, Yu Tsao

The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications.

Speech Enhancement

WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement

1 code implementation6 Apr 2020 Tsun-An Hsieh, Hsin-Min Wang, Xugang Lu, Yu Tsao

In WaveCRN, the speech locality feature is captured by a convolutional neural network (CNN), while the temporal sequential property of the locality feature is modeled by stacked simple recurrent units (SRU).

Denoising Speech Denoising +2

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