نتائج البحث - "Fang, Shih-Hau"

1

تقرير

Unsupervised Face-Mask Speech Enhancement Using Generative Adversarial Networks with Human-in-the-Loop Assessment Metrics

المؤلفون: Wang, Syu-Siang, Chen, Jia-Yang, Bai, Bo-Ren, Fang, Shih-Hau, Tsao, Yu

المصدر: IEEE/ACM Transactions on Audio, Speech and Language Processing, 2024

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing

الوصف: The utilization of face masks is an essential healthcare measure, particularly during times of pandemics, yet it can present challenges in communication in our daily lives. To address this problem, we propose a novel approach known as the human-in-the-loop StarGAN (HL-StarGAN) face-masked speech enhancement method. HL-StarGAN comprises discriminator, classifier, metric assessment predictor, and generator that leverages an attention mechanism. The metric assessment predictor, referred to as MaskQSS, incorporates human participants in its development and serves as a "human-in-the-loop" module during the learning process of HL-StarGAN. The overall HL-StarGAN model was trained using an unsupervised learning strategy that simultaneously focuses on the reconstruction of the original clean speech and the optimization of human perception. To implement HL-StarGAN, we curated a face-masked speech database named "FMVD," which comprises recordings from 34 speakers in three distinct face-masked scenarios and a clean condition. We conducted subjective and objective tests on the proposed HL-StarGAN using this database. The outcomes of the test results are as follows: (1) MaskQSS successfully predicted the quality scores of face mask voices, outperforming several existing speech assessment methods. (2) The integration of the MaskQSS predictor enhanced the ability of HL-StarGAN to transform face mask voices into high-quality speech; this enhancement is evident in both objective and subjective tests, outperforming conventional StarGAN and CycleGAN-based systems.
Comment: face-mask speech enhancement, generative adversarial networks, StarGAN, human-in-the-loop, unsupervised learning

الوصول الحر: http://arxiv.org/abs/2407.01939Test

View record in Arxiv

2

دورية أكاديمية

Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation

المؤلفون: Wang, Chi-Te, Han, Ji-Yan, Fang, Shih-Hau, Lai, Ying-Hui

المصدر: JMIR mHealth and uHealth, Vol 8, Iss 12, p e16746 (2020)

مصطلحات موضوعية: Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270

الوصف: BackgroundVoice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. ObjectiveThis study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. MethodsWe designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. ResultsThe proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. ConclusionsThis study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.

وصف الملف: electronic resource

العلاقة: https://mhealth.jmir.org/2020/12/e16746Test; https://doaj.org/toc/2291-5222Test

الوصول الحر: https://doaj.org/article/9c9c0ec56bb14402874fdf1be92f489fTest

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النص الكامل

3

تقرير

Continuous Speech for Improved Learning Pathological Voice Disorders

المؤلفون: Wang, Syu-Siang, Wang, Chi-Te, Lai, Chih-Chung, Tsao, Yu, Fang, Shih-Hau

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Sound, Quantitative Biology - Quantitative Methods

الوصف: Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). Methods: In the proposed framework, acoustic signals are transformed into mel-frequency cepstral coefficients, and a bi-directional long-short term memory network (BiLSTM) is adopted to model the sequential features. The experiments were conducted on a large-scale database, wherein 1,045 continuous speech were collected by the speech clinic of a hospital from 2012 to 2019. Results: Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall improvements of 78.12-89.27% and 50.92-80.68%, respectively, compared with systems that use a single vowel. Conclusions: The results are consistent with other machine learning algorithms, including gated recurrent units, random forest, deep neural networks, and LSTM. The sensitivities for each disorder were also analyzed, and the model capabilities were visualized via principal component analysis. An alternative experiment based on a balanced dataset again confirms the advantages of using continuous speech for learning voice disorders.

الوصول الحر: http://arxiv.org/abs/2202.10777Test

View record in Arxiv

4

تقرير

Toward Real-World Voice Disorder Classification

المؤلفون: Kuo, Heng-Cheng, Hsieh, Yu-Peng, Tseng, Huan-Hsin, Wang, Chi-Te, Fang, Shih-Hau, Tsao, Yu

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound

الوصف: Objective: Voice disorders significantly compromise individuals' ability to speak in their daily lives. Without early diagnosis and treatment, these disorders may deteriorate drastically. Thus, automatic classification systems at home are desirable for people who are inaccessible to clinical disease assessments. However, the performance of such systems may be weakened due to the constrained resources and domain mismatch between the clinical data and noisy real-world data. Methods: This study develops a compact and domain-robust voice disorder classification system to identify the utterances of health, neoplasm, and benign structural diseases. Our proposed system utilizes a feature extractor model composed of factorized convolutional neural networks and subsequently deploys domain adversarial training to reconcile the domain mismatch by extracting domain invariant features. Results: The results show that the unweighted average recall in the noisy real-world domain improved by 13% and remained at 80% in the clinic domain with only slight degradation. The domain mismatch was effectively eliminated. Moreover, the proposed system reduced the usage of both memory and computation by over 73.9%. Conclusion: By deploying factorized convolutional neural networks and domain adversarial training, domain-invariant features can be derived for voice disorder classification with limited resources. The promising results confirm that the proposed system can significantly reduce resource consumption and improve classification accuracy by considering the domain mismatch. Significance: To the best of our knowledge, this is the first study that jointly considers real-world model compression and noise-robustness issues in voice disorder classification. The proposed system is intended for application to embedded systems with limited resources.
Comment: Accepted by IEEE TBME (under an IEEE Open Access publishing Agreement)

الوصول الحر: http://arxiv.org/abs/2112.02538Test

View record in Arxiv

5

دورية أكاديمية

Prediction of Customer Behavior Changing via a Hybrid Approach

المؤلفون: Lee, Nien-Ting, Lee, Hau-Chen, Hsin, Joseph, Fang, Shih-Hau

المصدر: IEEE Open Journal of the Computer Society ; volume 5, page 27-38 ; ISSN 2644-1268

مصطلحات موضوعية: General Computer Science

الإتاحة: https://doi.org/10.1109/ojcs.2023.3336904Test
http://xplorestaging.ieee.org/ielx7/8782664/10375894/10334013.pdf?arnumber=10334013Test

View record in BASE

6

تقرير

Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencoder

المؤلفون: Tsai, Kun-Hsi, Wang, Wei-Chien, Cheng, Chui-Hsuan, Tsai, Chan-Yen, Wang, Jou-Kou, Lin, Tzu-Hao, Fang, Shih-Hau, Chen, Li-Chin, Tsao, Yu

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing

الوصف: Auscultation is the most efficient way to diagnose cardiovascular and respiratory diseases. To reach accurate diagnoses, a device must be able to recognize heart and lung sounds from various clinical situations. However, the recorded chest sounds are mixed by heart and lung sounds. Thus, effectively separating these two sounds is critical in the pre-processing stage. Recent advances in machine learning have progressed on monaural source separations, but most of the well-known techniques require paired mixed sounds and individual pure sounds for model training. As the preparation of pure heart and lung sounds is difficult, special designs must be considered to derive effective heart and lung sound separation techniques. In this study, we proposed a novel periodicity-coded deep auto-encoder (PC-DAE) approach to separate mixed heart-lung sounds in an unsupervised manner via the assumption of different periodicities between heart rate and respiration rate. The PC-DAE benefits from deep-learning-based models by extracting representative features and considers the periodicity of heart and lung sounds to carry out the separation. We evaluated PC-DAE on two datasets. The first one includes sounds from the Student Auscultation Manikin (SAM), and the second is prepared by recording chest sounds in real-world conditions. Experimental results indicate that PC-DAE outperforms several well-known separations works in terms of standardized evaluation metrics. Moreover, waveforms and spectrograms demonstrate the effectiveness of PC-DAE compared to existing approaches. It is also confirmed that by using the proposed PC-DAE as a pre-processing stage, the heart sound recognition accuracies can be notably boosted. The experimental results confirmed the effectiveness of PC-DAE and its potential to be used in clinical applications.
Comment: 13 pages, 11 figures, Accepted by IEEE Journal of Biomedical and Health Informatics

الوصول الحر: http://arxiv.org/abs/2012.06275Test

View record in Arxiv

7

تقرير

Distributed Microphone Speech Enhancement based on Deep Learning

المؤلفون: Wang, Syu-Siang, Liang, Yu-You, Hung, Jeih-weih, Tsao, Yu, Wang, Hsin-Min, Fang, Shih-Hau

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound

الوصف: Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE) systems based on deep neural networks (DNNs) applied to a distributed microphone architecture, and then investigates the effectiveness of three different DNN-model structures. The first system constructs a DNN model for each microphone to enhance the recorded noisy speech signal, and the second system combines all the noisy recordings into a large feature structure that is then enhanced through a DNN model. As for the third system, a channel-dependent DNN is first used to enhance the corresponding noisy input, and all the channel-wise enhanced outputs are fed into a DNN fusion model to construct a nearly clean signal. All the three DNN SE systems are operated in the acoustic frequency domain of speech signals in a diffuse-noise field environment. Evaluation experiments were conducted on the Taiwan Mandarin Hearing in Noise Test (TMHINT) database, and the results indicate that all the three DNN-based SE systems provide the original noise-corrupted signals with improved speech quality and intelligibility, whereas the third system delivers the highest signal-to-noise ratio (SNR) improvement and optimal speech intelligibility.
Comment: deep neural network, multi-channel speech enhancement, distributed microphone architecture, diffuse noise environment

الوصول الحر: http://arxiv.org/abs/1911.08153Test

View record in Arxiv

8

تقرير

Robustness against the channel effect in pathological voice detection

المؤلفون: Hsu, Yi-Te, Zhu, Zining, Wang, Chi-Te, Fang, Shih-Hau, Rudzicz, Frank, Tsao, Yu

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning

الوصف: Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect is a pervasive problem in machine learning for healthcare. In this study, we propose a detection system for pathological voice, which is robust against the channel effect. This system is based on a bidirectional LSTM network. To increase the performance robustness against channel mismatch, we integrate domain adversarial training (DAT) to eliminate the differences between the devices. When we train on data recorded on a high-quality microphone and evaluate on smartphone data without labels, our robust detection system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target sample labels). To the best of our knowledge, this is the first study applying unsupervised domain adaptation to pathological voice detection. Notably, our system does not need target device sample labels, which allows for generalization to many new devices.
Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

الوصول الحر: http://arxiv.org/abs/1811.10376Test

View record in Arxiv