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IEEE Signal processing in Medicine and Biology Symposium (SPMB)

作者： M. Mahmud M. Hasan M. Yeasin G. Bidelman Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Speech Language and Hearing Sciences Indiana University Bloomington IN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing.

关键词： Support vector machines Accuracy Hidden Markov models Phonetics Signal processing Electroencephalography Real-time systems Decoding speech processing Random forests

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Eye movement patterns are similar during accurate multiple-target tracking

Eye movement patterns are similar during accurate multiple-t...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Kamyar Bagha Shiva Kamkar Hamid Abrishami Moghaddam Lauri Oksama Jie Li Jukka Hyönä Computer Engineering Department Khatam University Tehran Iran Machine Vision and Medical Image Processing (MVMIP) Laboratory Faculty of Electrical Engineering K.N.Toosi University of Technology Tehran Iran Center for International Scientific Studies and Collaboration (CISSC) Tehran Iran Department of Psychology and Speech-Language Pathology University of Turku Turku Finland Center for Cognition and Brain Disorders Hangzhou Normal University Hangzhou China

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

Understanding how the brain works is a base of cognitive info-communication. To this aim we focus on multiple target tracking (MTT) as a key task that involves two important cognitive factors, attention and memory. Humans track multiple objects in their daily life while facing various challenges including occlusion and set-size. Eye movement research has shown that there are within and between subjects’ differences in scanpaths while performing MTT tasks. However, it is unclear if there is a winning scan pattern that would lead to a successful tracking of targets. To answer this question, we used dynamic time warping to compare the similarities between subjects’ scan patterns during an MTT task with different challenges. We studied the effect of set-size, occlusion, and trial response on the similarities. Then a mixed effect analysis was applied on the output to measure whether the findings were statistically significant. Results demonstrated that scan patterns were more similar when MTT task was performed correctly. It suggests that there is a common tracking strategy adopted by the viewers that leads to a correct response. Decoding this strategy has countless applications in the fields including human-computer interaction, brain-modeling and cognitive info-communication.

关键词： Human computer interaction Visualization Target tracking Accuracy Predictive models Communications technology Time measurement Cognition Decoding Cognitive science

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Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson’s disease

Concurrent validity of instrumented insoles measuring gait a...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sophia A. Watkinson Anthony Anderson Michael Caiola David Eguren Michael Gonzalez Laureano Moro Velazquez Najim Dehak Chelsea Motley Emile Moukheiber Kelly Mills Brittney Muir Ankur Butala Kimberly Kontson Division of Biomedical Physics US Food and Drug Administration MD USA Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA Department of Veterans Affairsy Center for Limb Loss and Mobility Seattle WA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Several commercially available instrumented insole systems have been examined for validity and repeatability, but very few studies have focused on validation of the Moticon OpenGo sensor insoles in measuring gait and balance parameters in a clinical population. Given the paucity of studies examining the validity of these novel technologies in PD, there were two main goals of this research: (1) assess the concurrent validity of the Moticon OpenGo sensor insoles for gait and balance assessment in people with PD using a pressure-sensitive electronic walkway (Protokinetics Zeno™ walkway) as a reference system and (2) compare the gait metrics derived from the insole and walkway systems during a walking and turning task in order to assess the output of the systems under more real-world conditions. Twelve participants (5F/7M; mean age 71.3 ± 6.8 years) with a diagnosis of Parkinson’s disease and a score of 2.5 or 3 on the Modified Hoehn & Yahr score performing straight-line walking, walking with turning, and balance tasks were included in this analysis. Differences in the estimated gait metrics from each system were evaluated through Bland-Altman analysis and calculation of Pearson’s correlation coefficient (r). Results showed strong agreement between the insoles and the reference walkway system during both self-selected pace and hurried pace walking tasks. Although agreement is not as strong when estimating spatial metrics, the limits of agreement (LOA) still indicate that clinically important differences can be seen. Additional investigation into the insole derived center of pressure metrics from the insole sensor systems may be necessary as well as investigation into improved algorithms for capturing gait metrics during more real-world conditions in both types of systems.

关键词： Measurement Legged locomotion Correlation coefficient Instruments Turning Sensor systems engineering in medicine and biology Diseases

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FedPOIRec: Privacy preserving federated POI recommendation with social influence

arXiv

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arXiv 2021年

作者： Perifanis, Vasileios Drosatos, George Stamatelatos, Giorgos Efraimidis, Pavlos S. Institute for Language and Speech Processing Athena Research Center Xanthi Kimmeria67100 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Kimmeria67100 Greece

With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs). Traditional systems consider a centralized approach that requires the transmission and collection of users' private data. In this work, we present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-N POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner's device, while the local updates are blindly aggregated by a parameter server. Second, the local recommenders get personalized by allowing users to exchange their learned parameters, enabling knowledge transfer among friends. To this end, we propose a privacy preserving protocol for integrating the preferences of a user's friends after the federated computation, by exploiting the properties of the CKKS fully homomorphic encryption scheme. To evaluate FedPOIRec, we apply our approach into five real-world datasets using two recommendation models. Extensive experiments demonstrate that FedPOIRec achieves comparable recommendation quality to centralized approaches, while the social integration protocol incurs low computation and communication overhead on the user side. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge management

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Discovering Phonetic Inventories with Crosslingual Automatic speech Recognition

arXiv

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arXiv 2022年

作者： Żelasko, Piotr Feng, Siyuan Velázquez, Laureano Moro Abavisani, Ali Bhati, Saurabhchand Scharenborg, Odette Hasegawa-Johnson, Mark Dehak, Najim Center of Language and Speech Processing The Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Human Language Technology Center of Excellence The Johns Hopkins University 810 Wyman Park Drive BaltimoreMD21218 United States Multimedia Computing Group Delft University of Technology Van Mourik Broekmanweg 6 Delft2628 XE Netherlands Department of Electrical and Computer Engineering University of Illinois 405 N Mathews UrbanaIL61801 United States

The high cost of data acquisition makes Automatic speech Recognition (ASR) model training problematic for most existing languages, including languages that do not even have a written script, or for which the phone inventories remain unknown. Past works explored multilingual training, transfer learning, as well as zero-shot learning in order to build ASR systems for these low-resource languages. While it has been shown that the pooling of resources from multiple languages is helpful, we have not yet seen a successful application of an ASR model to a language unseen during training. A crucial step in the adaptation of ASR from seen to unseen languages is the creation of the phone inventory of the unseen language. The ultimate goal of our work is to build the phone inventory of a language unseen during training in an unsupervised way without any knowledge about the language. In this paper, we 1) investigate the influence of different factors (i.e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language;2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation;and 3) present different methods to build a phone inventory of an unseen language in an unsupervised way. To that end, we conducted mono-, multi-, and crosslingual experiments on a set of 13 phonetically diverse languages and several in-depth analyses. We found a number of universal phone tokens (IPA symbols) that are well-recognized cross-linguistically. Through a detailed analysis of results, we conclude that unique sounds, similar sounds, and tone languages remain a major challenge for phonetic inventory discovery. © 2022, CC BY-NC-ND.

关键词： Telephone sets

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Prospectively accelerated dynamic speech MRI at 3 Tesla using a self-navigated spiral based manifold regularized scheme

arXiv

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arXiv 2022年

作者： Rusho, Rushdi Zahid Ahmed, Abdul Haseeb Kruger, Stanley Alam, Wahidul Meyer, David Howard, David Story, Brad Jacob, Mathews Lingala, Sajan Goud Roy J Carver Department of Biomedical Engineering University of Iowa Iowa city IA52242 United States Department of Electrical and Computer Engineering University of Iowa Iowa city IA52242 United States Department of Radiology University of Iowa Iowa city IA52242 United States Janette Ogg Voice Research Center Shenandoah University WinchesterVA22601 United States Department of Electronic Engineering Royal Holloway University of London United Kingdom Department of Speech Language and Hearing Sciences University of Arizona AZ85721 United States

This work develops and evaluates a self-navigated variable density spiral (VDS) based manifold regularization scheme to prospectively improve dynamic speech MRI at 3 Tesla. Short readout duration spirals (1.3 ms long) were used to minimize sensitivity to off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a SENSE-based non-local soft weighted temporal regularization scheme. Our approach was compared against view-sharing, low-rank, finite difference, extra-dimension-based sparsity reconstruction constraints. Under-sampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges were performed in a retrospective under-sampling experiment on one volunteer. For prospective under-sampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring were performed by three experts in voice research. Region of interest (ROI) analysis at articulator boundaries were performed in both the experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective under-sampling, a spatial resolution of 2.4mm2/pixel and a temporal resolution of 17.4 ms/frame for single slice imaging, and 52.2 ms/frame for concurrent 3-slice imaging were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared to all other reconstruction constraints

关键词： Navigation

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Measuring pitch extractors' response to frequency-modulated multi-component signals

arXiv

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arXiv 2022年

作者： Kawahara, Hideki Yatabe, Kohei Sakakibara, Ken-Ichi Kitamura, Tatsuya Banno, Hideki Morise, Masanori Center for Innovative Research and Liaison Wakayama University Wakayama640-8510 Japan Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo184-8588 Japan Department of Speech-Language-Hearing Therapy Health Science University of Hokkaido Hokkaido Sapporo061-0293 Japan Faculty of Intelligence and Informatics Konan University Hyogo Kobe658-8501 Japan Department of Information Engineering Meijo University Aichi Nagoya468-8502 Japan Department of Frontier Media Science Meiji University Tokyo164-8525 Japan

This article focuses on the research tool for investigating the fundamental frequencies of voiced sounds. We introduce an objective and informative measurement method of pitch extractors' response to frequency-modulated tones. The method uses a new test signal for acoustic system analysis. The test signal enables simultaneous measurement of the extractors' responses. They are the modulation frequency response and the total distortion, including intermodulation distortions. We applied this method to various pitch extractors and placed them on several performance maps. We used the proposed method to fine-tune one of the extractors to make it the best fit tool for scientific research of voice fundamental *** Codes 94A12, 93C80, 42-08 Copyright © 2022, The Authors. All rights reserved.

关键词： Frequency modulation

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Utilization of resting-state electroencephalography spectral power in convolutional neural networks for classification of primary progressive aphasia

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NeuroImage: Reports 2025年第1期5卷 100242-100242页

作者： Quinn, Christina Craik, Alex Tessmer, Rachel Henry, Maya L. Dial, Heather Department of Communication Sciences and Disorders University of Houston 3871 Holman St Houston 77204 TX United States Department of Electrical and Computer Engineering University of Houston 4226 Martin Luther King Boulevard Houston 77204 TX United States Geriatric Research Education and Clinical Center VA Pittsburgh Healthcare System University Drive C Pittsburgh 15240 PA United States Department of Speech Language and Hearing Sciences The University of Texas at Austin 2504a Whitis Ave a1100 Austin 78712 TX United States Department of Neurology The University of Texas at Austin Dell Medical School 1501 Red River St Austin 78712 TX United States

We investigated relative power spectral density (PSD) in primary progressive aphasia (PPA) in delta, theta, alpha, and beta frequency bands in eyes open and closed resting-state electroencephalography (EEG). Our aims were to assess whether discernible differences could be observed between each PPA variant and to determine the utility of PSD for PPA classification when used as input to a convolutional neural network (CNN). Findings in the current study were similar to previous studies in logopenic PPA, with a significant increase in relative PSD in delta and theta bands and a significant reduction in the beta band (consistent with oscillatory slowing). We did not observe a significant increase in power for lower frequency bands or a reduction of power in higher frequency bands for semantic or nonfluent PPA, in contrast to what has been previously reported. In semantic PPA, evidence pointed to oscillatory speeding, not the slowing that was previously reported in a single-case study. In nonfluent PPA, spectral power fell between logopenic and semantic PPA, suggesting there is oscillatory slowing but to a lesser extent than logopenic PPA. The CNN was relatively successful in distinguishing PPA from healthy controls (F1 = 0.851). The CNN did not perform as well on four-way classification (lvPPA, svPPA, nfvPPA, controls;F1 = 0.586) but was significantly above chance. These results are promising and suggest that resting-state EEG may prove useful as a biomarker for PPA diagnosis. Potential factors underlying the differences between the findings of the current study and previous work are discussed. © 2025 The Author(s)

关键词： Classification Deep learning Power spectral density Primary progressive aphasia Resting-state electroencephalography

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speech Enhancement via Deep Spectrum Image Translation Network 26

Speech Enhancement via Deep Spectrum Image Translation Netwo...

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26th National and 4th International Iranian Conference on Biomedical engineering, ICBME 2019

作者： Kashani, Hamidreza Baradaran Jodeiri, Ata Goodarzi, Mohammad Mohsen Rezaei, Iman Sarraf Amirkabir University of Technology Electrical Engineering Faculty Tehran Iran College of Engineering University of Tehran School of Electrical Computer Engineering Tehran Iran Buein Zahra Technical University Department of Electrical and Computer Engineering Qazvin Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (纸本)9781728156637

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptuallymodified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement methods in terms of both quality and intelligibility measures, represented by PESQ and ESTOI, respectively. © 2019 IEEE.

关键词： Audition

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Privacy-preserving solutions in the Industrial Internet of Things

Privacy-preserving solutions in the Industrial Internet of T...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： George Drosatos Konstantinos Rantos Dimitris Karampatzakis Thomas Lagkas Panagiotis Sarigiannidis Institute for Language and Speech Processing Athena Research Center Xanthi Greece Department of Computer Science International Hellenic University Kavala Greece Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9781728143514

ISBN: (纸本)9781728198040

Industrial Internet of Things (IIoT) is a relatively new area of research that utilises multidisciplinary and holistic approaches to develop smart solutions for complex problems in industrial environments. Designing applications for the IIoT is a non trivial issue and requires to address, among many others, technology concerns, the protection of personal data, and the privacy of individuals. In this review paper, we identify privacy-preserving solutions that have been proposed in the literature to safeguard the privacy of individuals being part, or interacting with, the IIoT environment. As such, it considers two main categories of the analysed protocols, i.e., the privacy-preserving data management and processing solutions, and the privacy-preserving authentication methods.

关键词： Privacy Encryption Authentication

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