Search Results for author: Yao-Fei Cheng

Found 5 papers, 3 papers with code

A Teacher-student Framework for Unsupervised Speech Enhancement Using Noise Remixing Training and Two-stage Inference

1 code implementation27 Oct 2022 Li-Wei Chen, Yao-Fei Cheng, Hung-Shin Lee, Yu Tsao, Hsin-Min Wang

The lack of clean speech is a practical challenge to the development of speech enhancement systems, which means that the training of neural network models must be done in an unsupervised manner, and there is an inevitable mismatch between their training criterion and evaluation metric.

Speech Enhancement

CasNet: Investigating Channel Robustness for Speech Separation

no code implementations27 Oct 2022 Fan-Lin Wang, Yao-Fei Cheng, Hung-Shin Lee, Yu Tsao, Hsin-Min Wang

In this study, inheriting the use of our previously constructed TAT-2mix corpus, we address the channel mismatch problem by proposing a channel-aware audio separation network (CasNet), a deep learning framework for end-to-end time-domain speech separation.

Speech Separation

Speech-enhanced and Noise-aware Networks for Robust Speech Recognition

1 code implementation25 Mar 2022 Hung-Shin Lee, Pin-Yuan Chen, Yao-Fei Cheng, Yu Tsao, Hsin-Min Wang

In this paper, a noise-aware training framework based on two cascaded neural structures is proposed to jointly optimize speech enhancement and speech recognition.

Automatic Speech Recognition Robust Speech Recognition +2

Chain-based Discriminative Autoencoders for Speech Recognition

no code implementations25 Mar 2022 Hung-Shin Lee, Pin-Tuan Huang, Yao-Fei Cheng, Hsin-Min Wang

For application to robust speech recognition, we further extend c-DcAE to hierarchical and parallel structures, resulting in hc-DcAE and pc-DcAE.

Robust Speech Recognition speech-recognition

AlloST: Low-resource Speech Translation without Source Transcription

1 code implementation1 May 2021 Yao-Fei Cheng, Hung-Shin Lee, Hsin-Min Wang

In this study, we survey methods to improve ST performance without using source transcription, and propose a learning framework that utilizes a language-independent universal phone recognizer.


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