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Correction to: Attention based video captioning framework for Hindi

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Multimedia Systems 2021年第1期29卷 453-453页

作者： Singh, Alok Singh, Thoudam Doren Bandyopadhyay, Sivaji Center for Natural Language Processing (CNLP) NIT Silchar India Department of Computer Science and Engineering National Institute of Technology Silchar India

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

MIMO-speech: End-to-end multi-channel multi-speaker speech recognition

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arXiv 2019年

作者： Chang, Xuankai Zhang, Wangyou Qian, Yanmin Le Roux, Jonathan Watanabe, Shinji Center for Language and Speech Processing Johns Hopkins University United States SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China United States

Recently, the end-to-end approach has proven its efficacy in monaural multi-speaker speech recognition. However, high word error rates (WERs) still prevent these systems from being used in practical applications. On the other hand, the spatial information in multi-channel signals has proven helpful in far-field speech recognition tasks. In this work, we propose a novel neural sequence-to-sequence (seq2seq) architecture, MIMO-speech, which extends the original seq2seq to deal with multi-channel input and multi-channel output so that it can fully model multi-channel multi-speaker speech separation and recognition. MIMO-speech is a fully neural end-to-end framework, which is optimized only via an ASR criterion. It is comprised of: 1) a monaural masking network, 2) a multi-source neural beamformer, and 3) a multi-output speech recognition model. With this processing, the input overlapped speech is directly mapped to text sequences. We further adopted a curriculum learning strategy, making the best use of the training set to improve the performance. The experiments on the spatialized wsj1-2mix corpus show that our model can achieve more than 60% WER reduction compared to the single-channel system with high quality enhanced signals (SI-SDR = 23.1 dB) obtained by the above separation function. Copyright © 2019, The Authors. All rights reserved.

关键词： Beamforming

Neural polysynthetic language modelling

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arXiv 2020年

作者： Schwartz, Lane Tyers, Francis Levin, Lori Kirov, Christo Littell, Patrick Lo, Chi-kiu Prud’hommeaux, Emily Park, Hyunji Hayley Steimel, Kenneth Knowles, Rebecca Micher, Jeffrey Strunk, Lonny Liu, Han Haley, Coleman Zhang, Katherine J. Jimerson, Robbie Andriyanets, Vasilisa Muis, Aldrian Obaja Otani, Naoki Park, Jong Hyuk Zhang, Zhisong Department of Linguistics University of Illinois at Urbana-Champaign Department of Linguistics Indiana University Language Technologies Institute Carnegie Mellon University Google National Research Council of Canada Department of Computer Science Boston College Center for Language and Speech Processing Johns Hopkins University National Research Council of Canada Army Research Laboratory Carnegie Mellon University Department of Linguistics University of Washington Department of Computer Science University of Colorado Boulder United States Johns Hopkins University Carnegie Mellon University Rochester Institute of Technology Moscow Higher School of Economics

Many techniques in modern computational linguistics and natural language processing (NLP) make the assumption that approaches that work well on English and other widely used European (and sometimes Asian) languages are "language agnostic" – that is that they will also work across the typologically diverse languages of the world. In high-resource languages, especially those that are analytic rather than synthetic, a common approach is to treat morphologically-distinct variants of a common root (such as dog and dogs) as completely independent word types. Doing so relies on two main assumptions: that there exist a limited number of morphological inflections for any given root, and that most or all of those variants will appear in a large enough corpus (conditioned on assumptions about domain, etc.) so that the model can adequately learn statistics about each variant. Approaches like stemming, lemmatization, morphological analysis, subword segmentation, or other normalization techniques are frequently used when either of those assumptions are likely to be violated, particularly in the case of synthetic languages like Czech and Russian that have more inflectional morphology than English. Within the NLP literature, agglutinative languages like Finnish and Turkish are commonly held up as extreme examples of morphological complexity that challenge common modelling assumptions. Yet, when considering all of the world’s languages, Finnish and Turkish are closer to the average case in terms of synthesis. When we consider polysynthetic languages (those at the extreme of morphological complexity), even approaches like stemming, lemmatization, or subword modelling may not suffice. These languages have very high numbers of hapax legomena (words appearing only once in a corpus), underscoring the need for appropriate morphological handling of words, without which there is no hope for a model to capture enough statistical information about those words. Moreover, many of these language

关键词： Natural language processing systems

Trends in Silicates in the β Pictoris Disk

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arXiv 2022年

作者： Lu, Cicero X. Chen, Christine H. Sargent, B.A. Watson, Dan M. Lisse, Carey M. Green, Joel D. Sitko, Michael L. Mittal, Tushar Lebouteiller, V. Sloan, G.C. Rebollido, Isabel Hines, Dean C. Girard, Julien H. Werner, Michael W. Stapelfeldt, Karl R. Wu, Winston Worthen, Kadin Department of Physics and Astronomy The Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Department of Physics and Astronomy University of Rochester 500 Wilson Blvd RochesterNY14627 United States Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd LaurelMD20723 United States Department of Physics University of Cincinnati CincinnatiOH45221 United States Space Science Institute BoulderCO80301 United States Mit Department of Earth Atmospheric and Planetary Sciences Green Bldg The 77 Massachusetts Ave CambridgeMA02139 United States Aim Cea Cnrs Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France Department of Physics and Astronomy University of North Carolina Chapel HillNC27599-3255 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91107 United States Center for Language and Speech Processing Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Computer Science and Engineering University of Michigan Ann ArborMI48109 United States

While β Pic is known to host silicates in ring-like structures, whether the properties of these silicate dust vary with stellocentric distance remains an open question. We re-analyze the β Pictoris debris disk spectrum from the Spitzer Infrared Spectrograph (IRS) and a new IRTF/SpeX spectrum to investigate trends in Fe/Mg ratio, shape, and crystallinity in grains as a function of wavelength, a proxy for stellocentric distance. By analyzing a recalibrated and re-extracted spectrum, we identify a new 18 μm forsterite emission feature and recover a 23 μm forsterite emission feature with a substantially larger line-to-continuum ratio than previously reported. We find that these prominent spectral features are primarily produced by small submicron-sized grains, which are continuously generated and replenished from planetesimal collisions in the disk and can elucidate their parent bodies' composition. We discover three trends about these small grains: as stellocentric distance increases, (1) small silicate grains become more crystalline (less amorphous), (2) they become more irregular in shape, and (3) for crystalline silicate grains, the Fe/Mg ratio decreases. Applying these trends to β Pic's planetary architecture, we find that the dust population exterior to the orbits of β Pic b and c differs substantially in crystallinity and shape. We also find a tentative 3-5 μm dust excess due to spatially unresolved hot dust emission close to the star. From our findings, we infer that the surfaces of large planetesimals are more Fe-rich and collisionally-processed closer to the star but more Fe-poor and primordial farther from the star. © 2022, CC BY.

关键词： Silicates

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

Morphological Word Embeddings

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arXiv 2019年

作者： Cotterell, Ryan Schütze, Hinrich Department of Computer Science Johns Hopkins University United States Center for Information and Language Processing University of Munich Germany

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semi-supervised learning, encouraging the vectors to encode a word’s morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

Incremental Lattice Determinization for WFST Decoders

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Incremental Lattice Determinization for WFST Decoders

IEEE Workshop on Automatic speech Recognition and Understanding

作者： Zhehuai Chen Mahsa Yarmohammadi Hainan Xu Hang Lv Lei Xie Daniel Povey Sanjeev Khudanpur SpeechLab Shanghai Jiao Tong University Center for Language and Speech Processing ASLP@NPU School of Computer Science Northwestern Polytechnical University HLTCOE Johns Hopkins University

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

We introduce a lattice determinization algorithm that can operate incrementally. That is, a word-level lattice can be generated for a partial utterance and then, once we have processed more audio, we can obtain a word-level lattice for the extended utterance without redoing all the work of lattice determinization. This is relevant for ASR decoders such as those used in Kaldi, which first generate a state-level lattice and then convert it to a word-level lattice using a determinization algorithm in a special semiring. Our incremental determinization algorithm is useful when word-level lattices are needed prior to the end of the utterance, and also reduces the latency due to determinization at the end of the utterance.

关键词： Lattices Decoding Real-time systems Automata Transducers Task analysis computer science

MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

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MultiplEYE: Creating a multilingual eye-tracking-while-readi...

Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit