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- research-articleAugust 2023
SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
- Tsai-Min Chen,
- Yuan-Hong Tsai,
- Huan-Hsin Tseng,
- Kai-Chun Liu,
- Jhih-Yu Chen,
- Chih-Han Huang,
- Guo-Yuan Li,
- Chun-Yen Shen,
- Yu Tsao
IEEE Transactions on Consumer Electronics (ITOCE), Volume 69, Issue 3Aug. 2023, Pages 250–260https://doi.org/10.1109/TCE.2023.3237715A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-...
- research-articleJune 2023
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 31Pages 2738–2750https://doi.org/10.1109/TASLP.2023.3288418Previous 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 broad phonetic properties of the input speech when performing ...
- research-articleSeptember 2022
Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 31Pages 54–70https://doi.org/10.1109/TASLP.2022.3205757This study proposes a cross-domain multi-objective speech assessment model, called MOSA-Net, which can simultaneously estimate the speech quality, intelligibility, and distortion assessment scores of an input speech signal. MOSA-Net comprises a ...
- research-articleFebruary 2022
Improved Lite Audio-Visual Speech Enhancement
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 30Pages 1345–1359https://doi.org/10.1109/TASLP.2022.3153265Numerous studies have investigated the effectiveness of audio-visual multimodal learning for speech enhancement (AVSE) tasks, seeking a solution that uses visual data as auxiliary and complementary input to reduce the noise of noisy speech signals. ...
- research-articleDecember 2021
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 30Pages 1016–1031https://doi.org/10.1109/TASLP.2021.3133209Numerous compression and acceleration strategies have achieved outstanding results on classification tasks in various fields. Nevertheless, the same strategies may yield unsatisfactory performance on regression tasks because the nature between regression ...
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- research-articleJune 2024
Unsupervised noise adaptive speech enhancement by discriminator-constrained optimal transport
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing SystemsDecember 2021, Article No.: 1525, Pages 19935–19946This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing. The DOTN aims to estimate clean ...
- research-articleNovember 2021
Coupling a Generative Model With a Discriminative Learning Framework for Speaker Verification
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 29Pages 3631–3641https://doi.org/10.1109/TASLP.2021.3129360The task of speaker verification (SV) is to decide whether an utterance is spoken by a target or an imposter speaker. In most studies of SV, a log-likelihood ratio (LLR) score is estimated based on a generative probability model on speaker features, and ...
- research-articleDecember 2020
Cross-Technology Interference Mitigation Using Fully Convolutional Denoising Autoencoders
GLOBECOM 2020 - 2020 IEEE Global Communications ConferenceDec 2020, Pages 1–6https://doi.org/10.1109/GLOBECOM42002.2020.9322340Cross-Technology Interference (CTI) is one of the major issues that hinder WiFi networks from achieving full spectrum utilization. Interference from nearby ZigBee devices, LTE-U UEs or even microwave ovens could emit RF signals over the frequency ...
- research-articleDecember 2020
Subspace-Based Representation and Learning for Phonotactic Spoken Language Recognition
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 28Pages 3065–3079https://doi.org/10.1109/TASLP.2020.3037457Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events. In the present study, we propose a new learning mechanism based on subspace-based representation, which can ...
- research-articleOctober 2020
Speech Enhancement Based on Denoising Autoencoder With Multi-Branched Encoders
- Cheng Yu,
- Ryandhimas E. Zezario,
- Syu-Siang Wang,
- Jonathan Sherman,
- Yi-Yen Hsieh,
- Xugang Lu,
- Hsin-Min Wang,
- Yu Tsao
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 28Pages 2756–2769https://doi.org/10.1109/TASLP.2020.3025638Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model generalizability to noisy conditions: (1) mismatched noisy condition during ...
- research-articleJune 2020
Multichannel Speech Enhancement by Raw Waveform-Mapping Using Fully Convolutional Networks
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 28Pages 1888–1900https://doi.org/10.1109/TASLP.2020.2976193In recent years, waveform-mapping-based speech enhancement (SE) methods have garnered significant attention. These methods generally use a deep learning model to directly process and reconstruct speech waveforms. Because both the input and output are in ...
- research-articleSeptember 2018
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 26, Issue 9Pages 1570–1584https://doi.org/10.1109/TASLP.2018.2821903Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in the existing literature, there is an inconsistency between the model ...
- research-articleApril 2018
Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Apr 2018, Pages 5454–5458https://doi.org/10.1109/ICASSP.2018.8462662Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the ...
- research-articleApril 2018
Enhancement and Analysis of Conversational Speech: JSALT 2017
- Neville Ryanta,
- Elika Bergelson,
- Kenneth Church,
- Alejandrina Cristia,
- Jun Du,
- Sriram Ganapathy,
- Sanjeev Khudanpur,
- Diana Kowalski,
- Mahesh Krishnamoorthy,
- Rajat Kulshreshta,
- Mark Liberman,
- Yu-Ding Lu,
- Matthew Maciejewski,
- Florian Metze,
- Jan Profant,
- Lei Sun,
- Yu Tsao,
- Zhou Yu
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Apr 2018, Pages 5154–5158https://doi.org/10.1109/ICASSP.2018.8462468Automatic speech recognition is more and more widely and effectively used. Nevertheless, in some automatic speech analysis tasks the state of the art is surprisingly poor. One of these is “diarization”, the task of determining who spoke ...
- research-articleApril 2018
A Novel LSTM-Based Speech Preprocessor for Speaker Diarization in Realistic Mismatch Conditions
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Apr 2018, Pages 5234–5238https://doi.org/10.1109/ICASSP.2018.8462311In this study, we investigate on the effects of deep learning based speech enhancement as a preprocessor to speaker diarization in quite challenging realistic environments involving the background noises, reverberations and overlapping speech. To improve ...
- research-articleMarch 2018
Suppression by Selecting Wavelets for Feature Compression in Distributed Speech Recognition
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 26, Issue 3Pages 564–579https://doi.org/10.1109/TASLP.2017.2779787Distributed speech recognition DSR splits the processing of data between a mobile device and a network server. In the front-end, features are extracted and compressed to transmit over a wireless channel to a back-end server, where the incoming stream is ...
- research-articleNovember 2017
Multi-style learning with denoising autoencoders for acoustic modeling in the internet of things (IoT)
Computer Speech and Language (CSPL), Volume 46, Issue CNovember 2017, Pages 481–495https://doi.org/10.1016/j.csl.2017.02.001Multi-style Learning (Multi-style training+Deep Learning) relies on multiple layers of nonlinear data transformations to synthesize additional examples of representative variations.Rather than artificially creating data samples, a deep denoising ...
- research-articleJuly 2017
Regularization of neural network model with distance metric learning for i-vector based spoken language identification
Computer Speech and Language (CSPL), Volume 44, Issue CJuly 2017, Pages 48–60https://doi.org/10.1016/j.csl.2017.01.006Pair-wise distance metric learning was designed on the feature transform layers.Stacking a soft-max layer on the feature transform layers as a classifier layer.Learning the coupled functions in feature extraction and classification layers with a ...
- research-articleMarch 2017
A locally linear embbeding based postfiltering approach for speech enhancement
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Mar 2017, Pages 5555–5559https://doi.org/10.1109/ICASSP.2017.7953219This paper presents a novel postfiltering approach based on the locally linear embedding (LLE) algorithm for speech enchantment (SE). The aim of the proposed LLE-based postfiltering approach is to further remove the residual noise components from the SE-...
- research-articleMarch 2017
Discriminative autoencoders for speaker verification
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Mar 2017, Pages 5375–5379https://doi.org/10.1109/ICASSP.2017.7953183This paper presents a learning and scoring framework based on neural networks for speaker verification. The framework employs an autoencoder as its primary structure while three factors are jointly considered in the objective function for speaker ...