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Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio speech and language processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

Non causal deep learning based dereverberation

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arXiv 2020年

作者： Wuth, Jorge Stern, Richard M. Yoma, Nestor Becerra Speech Processing and Transmission Laboratory Electrical Engineering Department University of Chile Santiago Chile Department of Electrical and Computer Engineering Language Technologies Institute Carnegie Mellon University PittsburghPA15206 United States

In this paper we demonstrate the effectiveness of non-causal context for mitigating the effects of reverberation in deep-learning-based automatic speech recognition (ASR) systems. First, the value of non-causal context using a non-causal FIR filter is shown by comparing the contributions of previous vs. future information. Second, MLP- and LSTM-based dereverberation networks were trained to confirm the effects of causal and non-causal context when used in ASR systems trained with clean speech. The non-causal deep-learning-based dereverberation provides a 45% relative reduction in word error rate (WER) compared to the popular weighted prediction error (WPE) method in experiments with clean training in the REVERB challenge. Finally, an expanded multicondition training procedure used in combination with a semi-enhanced test utterance generation based on combinations of reverberated and dereverberated signals is proposed to reduce any artifacts or distortion that may be introduced by the non-causal dereverberation methods. The combination of both approaches provided average relative reductions in WER equal to 10.9% and 6.0% when compared to the baseline system obtained with the most recent REVERB challenge recipe without and with WPE, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： FIR filters

Privacy-preserving solutions in the Industrial Internet of Things

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

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

The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets

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arXiv 2020年

作者： Xu, Paiheng Dredze, Mark Broniatowski, David A. Malone Center for Engineering in Healthcare Center for Language and Speech Processing Department of Computer Science Johns Hopkins University Department of Engineering Management and Systems Engineering Institute for Data Democracy and Politics The George Washington University

Social distancing is an important component of the response to the novel Coronavirus (COVID-19) pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads, and"flattens the curve" such that the medical system can better treat infected individuals. However, it remains unclear how the public will respond to these policies. This paper presents the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We use public geolocated Twitter data to measure how much a user travels in a given week. We find a large reduction in travel in the United States after the implementation of social distancing policies, with larger reductions in states that were early adopters and smaller changes in states without policies. Our findings are presented on http://*** and we will continue to update our analysis during the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

speech Enhancement via Deep Spectrum Image Translation Network 26

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Speech Enhancement via Deep Spectrum Image Translation Netwo...

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

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

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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

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

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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

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

Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN

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arXiv 2022年

作者： Mo, Yaozong Li, Chaofeng Ren, Wenqi Shang, Shaopeng Wang, Wenwu Wu, Xiao-Jun The Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai201306 China The School of Cyber Science and Technology Sun Yatsen University Shenzhen518000 China The Vocational College Shanghai Jian Qiao University Shanghai201306 China The Center for Vision Speech and Signal Processing Department of Electrical and Electronic Engineering University of Surrey SurreyGU2 7XH United Kingdom The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks