Search Results for author: Szu-Wei Fu

Found 33 papers, 15 papers with code

Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech

no code implementations26 Feb 2024 Szu-Wei Fu, Kuo-Hsuan Hung, Yu Tsao, Yu-Chiang Frank Wang

To improve the robustness of the encoder for SE, a novel self-distillation mechanism combined with adversarial training is introduced.

Quantization Speech Enhancement

Multi-objective Non-intrusive Hearing-aid Speech Assessment Model

no code implementations15 Nov 2023 Hsin-Tien Chiang, Szu-Wei Fu, Hsin-Min Wang, Yu Tsao, John H. L. Hansen

Furthermore, we demonstrated that incorporating SSL models resulted in greater transferability to OOD dataset.

A Study on Incorporating Whisper for Robust Speech Assessment

1 code implementation22 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.

Self-Supervised Learning

Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility

no code implementations10 Jul 2023 Hsin-Tien Chiang, Kuo-Hsuan Hung, Szu-Wei Fu, Heng-Cheng Kuo, Ming-Hsueh Tsai, Yu Tsao

Moreover, new objective measures are proposed that combine current objective measures using deep learning techniques to predict subjective quality and intelligibility.

QuAVF: Quality-aware Audio-Visual Fusion for Ego4D Talking to Me Challenge

1 code implementation30 Jun 2023 Hsi-Che Lin, Chien-Yi Wang, Min-Hung Chen, Szu-Wei Fu, Yu-Chiang Frank Wang

This technical report describes our QuAVF@NTU-NVIDIA submission to the Ego4D Talking to Me (TTM) Challenge 2023.

Improving Meeting Inclusiveness using Speech Interruption Analysis

no code implementations2 Apr 2023 Szu-Wei Fu, Yaran Fan, Yasaman Hosseinkashi, Jayant Gupchup, Ross Cutler

In order to drive adoption of its usage to improve inclusiveness (and participation), we present a machine learning-based system that predicts when a meeting participant attempts to obtain the floor, but fails to interrupt (termed a `failed interruption').

Real-time Speech Interruption Analysis: From Cloud to Client Deployment

no code implementations24 Oct 2022 Quchen Fu, Szu-Wei Fu, Yaran Fan, Yu Wu, Zhuo Chen, Jayant Gupchup, Ross Cutler

Meetings are an essential form of communication for all types of organizations, and remote collaboration systems have been much more widely used since the COVID-19 pandemic.

Boosting Self-Supervised Embeddings for Speech Enhancement

1 code implementation7 Apr 2022 Kuo-Hsuan Hung, Szu-Wei Fu, Huan-Hsin Tseng, Hsin-Tien Chiang, Yu Tsao, Chii-Wann Lin

We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE.

Self-Supervised Learning Speech Enhancement

Perceptual Contrast Stretching on Target Feature for Speech Enhancement

1 code implementation31 Mar 2022 Rong Chao, Cheng Yu, Szu-Wei Fu, Xugang Lu, Yu Tsao

Specifically, the contrast of target features is stretched based on perceptual importance, thereby improving the overall SE performance.

Speech Enhancement

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

SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points

no code implementations8 Nov 2021 Yu-Chen Lin, Cheng Yu, Yi-Te Hsu, Szu-Wei Fu, Yu Tsao, Tei-Wei Kuo

In this paper, a novel sign-exponent-only floating-point network (SEOFP-NET) technique is proposed to compress the model size and accelerate the inference time for speech enhancement, a regression task of speech signal processing.

Model Compression regression +1

Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features

1 code implementation3 Nov 2021 Ryandhimas E. Zezario, Szu-Wei Fu, Fei Chen, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao

In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.

Speech Enhancement

MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech

2 code implementations12 Oct 2021 Szu-Wei Fu, Cheng Yu, Kuo-Hsuan Hung, Mirco Ravanelli, Yu Tsao

Most of the deep learning-based speech enhancement models are learned in a supervised manner, which implies that pairs of noisy and clean speech are required during training.

Speech Enhancement

MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement

3 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

STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model

1 code implementation9 Nov 2020 Ryandhimas E. Zezario, Szu-Wei Fu, Chiou-Shann Fuh, Yu Tsao, Hsin-Min Wang

To overcome this limitation, we propose a deep learning-based non-intrusive speech intelligibility assessment model, namely STOI-Net.

Test

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

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

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

iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric Learning

1 code implementation Interspeech 2020 Haoyu Li, Szu-Wei Fu, Yu Tsao, Junichi Yamagishi

The intelligibility of natural speech is seriously degraded when exposed to adverse noisy environments.

Audio and Speech Processing Sound

Time-Domain Multi-modal Bone/air Conducted Speech Enhancement

no code implementations22 Nov 2019 Cheng Yu, Kuo-Hsuan Hung, Syu-Siang Wang, Szu-Wei Fu, Yu Tsao, Jeih-weih Hung

Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE).

Ensemble Learning Speech Enhancement

Improving the Intelligibility of Electric and Acoustic Stimulation Speech Using Fully Convolutional Networks Based Speech Enhancement

no code implementations26 Sep 2019 Natalie Yu-Hsien Wang, Hsiao-Lan Sharon Wang, Tao-Wei Wang, Szu-Wei Fu, Xugan Lu, Yu Tsao, Hsin-Min Wang

Recently, a time-domain speech enhancement algorithm based on the fully convolutional neural networks (FCN) with a short-time objective intelligibility (STOI)-based objective function (termed FCN(S) in short) has received increasing attention due to its simple structure and effectiveness of restoring clean speech signals from noisy counterparts.

Denoising Speech Enhancement +1 Sound Audio and Speech Processing

Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques

no code implementations31 May 2019 Jyun-Yi Wu, Cheng Yu, Szu-Wei Fu, Chih-Ting Liu, Shao-Yi Chien, Yu Tsao

In addition, a parameter quantization (PQ) technique was applied to reduce the size of a neural network by representing weights with fewer cluster centroids.

Denoising Quantization +1

MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement

5 code implementations13 May 2019 Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin

Adversarial loss in a conditional generative adversarial network (GAN) is not designed to directly optimize evaluation metrics of a target task, and thus, may not always guide the generator in a GAN to generate data with improved metric scores.

Generative Adversarial Network Speech Enhancement

Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality

1 code implementation6 May 2019 Szu-Wei Fu, Chien-Feng Liao, Yu Tsao

Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently.

Speech Enhancement

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

6 code implementations17 Apr 2019 Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang, Xin Wang, Junichi Yamagishi, Yu Tsao, Hsin-Min Wang

In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.

Test Voice Conversion

A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)

no code implementations17 Aug 2018 Yi-Te Hsu, Yu-Chen Lin, Szu-Wei Fu, Yu Tsao, Tei-Wei Kuo

We evaluated the proposed EOFP quantization technique on two types of neural networks, namely, bidirectional long short-term memory (BLSTM) and fully convolutional neural network (FCN), on a speech enhancement task.

Quantization regression +1

End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks

no code implementations12 Sep 2017 Szu-Wei Fu, Tao-Wei Wang, Yu Tsao, Xugang Lu, Hisashi Kawai

For example, in measuring speech intelligibility, most of the evaluation metric is based on a short-time objective intelligibility (STOI) measure, while the frame based minimum mean square error (MMSE) between estimated and clean speech is widely used in optimizing the model.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Complex spectrogram enhancement by convolutional neural network with multi-metrics learning

no code implementations27 Apr 2017 Szu-Wei Fu, Ting-yao Hu, Yu Tsao, Xugang Lu

This paper aims to address two issues existing in the current speech enhancement methods: 1) the difficulty of phase estimations; 2) a single objective function cannot consider multiple metrics simultaneously.

Speech Enhancement

Raw Waveform-based Speech Enhancement by Fully Convolutional Networks

no code implementations7 Mar 2017 Szu-Wei Fu, Yu Tsao, Xugang Lu, Hisashi Kawai

Because the fully connected layers, which are involved in deep neural networks (DNN) and convolutional neural networks (CNN), may not accurately characterize the local information of speech signals, particularly with high frequency components, we employed fully convolutional layers to model the waveform.

Denoising Speech Enhancement

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