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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Dan Su Pascale Fung Nicolas Auguin Human Language Technology Center Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

ISBN: (纸本)9781479903573

We propose using AdaBoost with decision stumps to implement multimodal music emotion classification (MEC) as a more appropriate alternative to the conventional SVMs. By modeling the presence or absence of salient phrases in the lyric texts and seeking for proper thresholds for certain audio signal features, it exploits interdependencies between aspects from both modalities in the multimodal MEC system to make the final classification. It can especially prevent the "short text problem" in lyrics. Our accuracy reached an average of 78.19% for classifying 3766 unique songs into 14 emotion categories, with a statistically significant improvement over the audio-only and lyrics-only monomodal MEC systems. We also show that the proposed AdaBoost with decision stumps method performs statistically better on multimodal MEC than the well-known SVM classifier, which only has an average accuracy of 72.08%.

关键词： AdaBoost Decision Stumps Music Emotion Multimodal ADABOOST Emotions Music most economic control CCL28 gene Taxonomy audio signals Multiple signal classification

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A CRITICAL EVALUATION OF STOCHASTIC ALGORITHMS FOR CONVEX OPTIMIZATION

A CRITICAL EVALUATION OF STOCHASTIC ALGORITHMS FOR CONVEX OP...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Simon Wiesler Alexander Richard Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479903573

Log-linear models find a wide range of applications in pattern recognition. The training of log-linear models is a convex optimization problem. In this work, we compare the performance of stochastic and batch optimization algorithms. Stochastic algorithms are fast on large data sets but can not be parallelized well. In our experiments on a broadcast conversations recognition task, stochastic methods yield competitive results after only a short training period, but when spending enough computational resources for parallelization, batch algorithms are competitive with stochastic algorithms. We obtained slight improvements by using a stochastic second order algorithm. Our best log-linear model outperforms the maximum likelihood trained Gaussian mixture model baseline although being ten times smaller.

关键词： discriminative models optimization speech recognition

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Comparing and evaluating semantic data automatically extracted from text

Comparing and evaluating semantic data automatically extract...

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2013 AAAI Fall Symposium

作者： Lawrie, Dawn Finin, Tim Mayfield, James McNamee, Paul Computer Science Department Loyola University Maryland Baltimore MD United States University of Maryland Baltimore County Baltimore MD United States Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD United States

ISBN: (纸本)9781577356424

One way to obtain large amounts of semantic data is to extract facts from the vast quantities of text that is now available on-line. The relatively low accuracy of current information extraction techniques introduces a need for evaluating the quality of the knowledge bases (KBs) they generate. We frame the problem as comparing KBs generated by different systems from the same documents and show that exploiting provenance leads to more efficient techniques for aligning them and identifying their differences. We describe two types of tools: entity-match focuses on differences in entitles found and linked;kbdiff focuses on differences in relations among those entities. Together, these tools support assessment of relative KB accuracy by sampling the parts of two KBs that disagree. We explore the usefulness of the tools through the construction of tens of different KBs built from the same 26,000 Washington Post articles and identifying the differences. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Semantics

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DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781479903573

Hierarchical Multi Layer Perceptron (MLP) based long-term feature extraction is optimized for TANDEM connectionist large vocabulary continuous speech recognition (LVCSR) system within the QUAERO project. Training the bottleneck MLP on multi-resolutional RASTA filtered critical band energies, more than 20% relative word error rate (WER) reduction over standard MFCC system is observed after optimizing the number of target labels. Furthermore, introducing a deeper structure in the hierarchical bottleneck processing the relative gain increases to 25%. The final system based on deep bottleneck TANDEM features clearly outperforms the hybrid approach, even if the long-term features are also presented to the deep MLP acoustic model. The results are also verified on evaluation data of the year 2012, and about 20% relative WER improvement over classical cepstral system is measured even after speaker adaptive training.

关键词： LVCSR MRASTA MLP bottleneck hierarchical deep neural network hybrid TANDEM Traffic Bottlenecks CSRP3 gene Hierarchical HYBRID Tandem relative gain

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INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER MATCHED AND MISMATCHED ACOUSTICAL CONDITIONS

INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Joel Pinto Daniel Willett Ralf Schluter Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Nuance Communications Deutschland GmbH

ISBN: (纸本)9781479903573

In this paper, Multi Layer Perceptron (MLP) based multilingual bottleneck features are investigated for acoustic modeling in three languages -- German, French, and US English. We use a modified training algorithm to handle the multilingual training scenario without having to explicitly map the phonemes to a common phoneme set. Furthermore, the cross-lingual portability of bottleneck features between the three languages are also investigated. Single pass recognition experiments on large vocabulary SMS dictation task indicate that (1) multilingual bottleneck features yield significantly lower word error rates compared to standard MFCC features (2) multilingual bottleneck features are superior to monolingual bottleneck features trained for the target language with limited training data, and (3) multilingual bottleneck features are beneficial in training acoustic models in a low resource language where only mismatched training data is available - by exploiting the more matched training data from other languages.

关键词： MLP Bottleneck Multilingual Mismatched acoustical condition Traffic Bottlenecks CSRP3 gene Multilingualism Object oriented programming languages language acoustic modeling Training data Phonemes Error analysis

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Proceedings of the 8th Annual Conference on the science of Dissemination and Implementation : Washington, DC, USA. 14-15 December 2015

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Implementation science : IS 2016年第2期11 Suppl 2卷 100页

作者： Chambers, David Simpson, Lisa Hill-Briggs, Felicia Neta, Gila Vinson, Cynthia Beidas, Rinad Marcus, Steven Aarons, Gregory Hoagwood, Kimberly Schoenwald, Sonja Evans, Arthur Hurford, Matthew Rubin, Ronnie Hadley, Trevor Barg, Frances Walsh, Lucia Adams, Danielle Mandell, David Martin, Lindsey Mignogna, Joseph Mott, Juliette Hundt, Natalie Kauth, Michael Kunik, Mark Naik, Aanand Cully, Jeffrey McGuire, Alan White, Dominique Bartholomew, Tom McGrew, John Luther, Lauren Rollins, Angie Salyers, Michelle Cooper, Brittany Funaiole, Angie Richards, Julie Lee, Amy Lapham, Gwen Caldeiro, Ryan Lozano, Paula Gildred, Tory Achtmeyer, Carol Ludman, Evette Addis, Megan Marx, Larry Bradley, Katharine VanDeinse, Tonya Wilson, Amy Blank Stacey, Burgin Powell, Byron Bunger, Alicia Cuddeback, Gary Barnett, Miya Stadnick, Nicole Brookman-Frazee, Lauren Lau, Anna Dorsey, Shannon Pullmann, Michael Mitchell, Shannon Schwartz, Robert Kirk, Arethusa Dusek, Kristi Oros, Marla Hosler, Colleen Gryczynski, Jan Barbosa, Carolina Dunlap, Laura Lounsbury, David O’Grady, Kevin Brown, Barry Damschroder, Laura Waltz, Thomas Ritchie, Mona Atkins, David Imel, Zac E. Xiao, Bo Can, Doğan Georgiou, Panayiotis Narayanan, Shrikanth Berkel, Cady Gallo, Carlos Sandler, Irwin Brown, C. Hendricks Wolchik, Sharlene Mauricio, Anne Marie Mehrotra, Sanjay Chandurkar, Dharmendra Bora, Siddhartha Das, Arup Tripathi, Anand Saggurti, Niranjan Raj, Anita Hughes, Eric Jacobs, Brian Kirkendall, Eric Loeb, Danielle Trinkley, Katy Yang, Michael Sprowell, Andrew Nease, Donald Lyon, Aaron Lewis, Cara Boyd, Meredith Melvin, Abigail Nicodimos, Semret Liu, Freda Jungbluth, Nathanial Flynn, Allen Landis-Lewis, Zach Sales, Anne Baloh, Jure Ward, Marcia Zhu, Xi Bennett, Ian Unutzer, Jurgen Mao, Johnny Proctor, Enola Vredevoogd, Mindy Chan, Ya-Fen Williams, Nathaniel Green, Phillip Bernstein, Steven Rosner, June-Marie DeWitt, Michelle Tetrault, Jeanette Dziura, James Hsiao, Allen Sussman, Scott O’Connor, Patrick Toll, Benjamin Jones, Michael Gassaway, Julie Tobin, Jonathan Zatzic Division of Cancer Control and Population Sciences National Cancer Institute National Institutes of Health Rockville USA AcademyHealth Washington USA Department of Medicine and Welch Center for Prevention Epidemiology and Clinical Research Johns Hopkins Medical Institutions Baltimore USA Psychiatry University of Pennsylvania Philadelphia USA Family Medicine and Community Health University of Pennsylvania Philadelphia USA School of Social Policy and Practice University of Pennsylvania Philadelphia USA Psychiatry UC San Diego La Jolla USA Child & Adolescent Psychiatry The Child Study Center at NYU Langone Medical Center New York NY New York USA Psychiatry and Behavioral Sciences MUSC Charleston USA DBHIDS City of Philadelphia Philadelphia USA CBH City of Philadelphia Philadelphia USA Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia USA Health Services Research & Development Department of Veterans Affairs & Baylor College of Medicine Houston USA Treatment Core Department of Veterans Affairs Waco USA National Center for PTSD Executive Division Department of Veterans Affairs White River Junction USA HSR&D HSR&D Center for Health Information and Communication Roudebush VA Medical Center Indianapolis USA Psychology Indiana University Purdue University Indianapolis Indianapolis USA Department of Psychiatric Rehabilitation and Counseling Rutgers University Newark USA Health Services Research & Development Richard L. Roudebush VAMC Indianapolis USA IUPUI Department of Psychology ACT Center of Indiana Indianapolis USA Human Development Washington State University Pullman USA Prevention Science Washington State University Pullman USA Group Health Research Institute Group Health Seattle USA Behavioral Health Services Group Health Seattle USA Preventive Care Group Health Seattle USA Health Services Research & Development Primary and Specialty Medical Care Service VA Puget Sound Seattle USA School of Social Work Un

A1 Introduction to the 8th Annual Conference on the science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&I research Felicia Hill-Briggs D2 Discussion forum: Global health D&I research Gila Neta, Cynthia Vinson D3 Discussion forum: Precision medicine and D&I research David Chambers S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach Brittany Cooper, Angie Funaiole S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau S8 Leveraging an existing

关键词： Veteran Affair Motivational Interview Qualitative Comparative Analysis Community That Care Supplemental Nutrition Assistance Program

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Poster abstracts

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Sleep and Biological Rhythms 2016年第1期5卷 A1-A189页

作者： J. Ferrie M. Shipley F. Cappuccio E. Brunner M. Miller M. Kumari M. Marmot A. Coenen J. L. Castillo F. Araya G. Bustamante L. Montecino C. Torres S. Oporto J. Gronli E. Fiske R. Murison B. Bjorvatn E. Sorensen R. Ursin C. M. Portas S. Rajaraman A. Gribok N. Wesensten T. Balkin J. Reifman N. Dursunoglu S. Ozkurt S. Baser O. Delen S. Sarikaya P. Sadler P. Mitchell D. Françon M. Decobert B. Herve A. Richard G. Griebel P. Avenet B. Scatton G. L. Fur D. Eckert A. Jordan A. Wellman S. Smith A. Malhotra D. White D. Bruck I. Thomas A. Kritikos W. Oertel K. Stiasny-Kolster D. Garcia-Borreguero W. Poewe B. Hoegl R. Kohnen E. Schollmayer J. Keffel C. Trenkwalder A. Valle S. Roizenblatt F. Fregni P. Boggio S. Tufik K. Ward L. Robertson L. Palmer P. Eastwood D. Hillman J. Lee S. Mukherjee V. de Padova G. Barbato G. Ficca I. Zilli P. Salzarulo M. Veldi T. Hion V. Vasar M. Kull L. Nowak J. Davis Y. Latzer O. Tzischinsky S. Crowley M. Carskadon M. Anca-Herschkovitsch D. Frey J. Ortega C. Wiseman C. Farley K. Wright A. Campbell A. Neill K. Spiegel R. Leproult E. Tasali N. Scherberg E. van Cauter A. T. Noradina N. A. Karim I. Norlinah A. A. Raymond R. Sahathevan B. Hamidon E. Werth R. Poryazova R. Khatami C. Bassetti R. G. Beran L. Ainley G. Holand J. Duncan H. Kinney B. Davis B. Hood S. Frey C. Schmidt M. Hofstetter P. Peigneux C. Cajochen W.-P. Hu J.-D. Li C. Zhang L. Boehmer J. Siegel Q.-Y. Zhou Y. Sagawa H. Kondo T. Takemura H. Kanayama Y. Kaneko M. Sato T. Kanbayashi Y. Hishikawa T. Shimizu A. Viola L. James L. Schlangen D.-J. Dijk R. Andretic Y.-C. Kim K.-A. Han F. Jones R. Greenspan L. Sanford L. Yang X. Tang K. Dieter E. Uta H. Sven M. Richard N. Oyane S. Pallesen F. Holsten Y. Inoue M. Fujita N. Emura K. Kuroda N. Uchimura A. Johnston J. Astbury G. Kennedy K. Hoedlmoser M. Schabus T. Pecherstorfer S. Moser G. Gruber P. Anderer W. Klimesch N. Naidoo M. Ferber A. Pack D. Neu O. Mairesse G. Hoffmann A. Dris L. Lambrecht P. Linkowski P. Verbanck O. Le Bon N. Matsuura M. Yamao N. Adachi R. Aritomi Y. Komada H. Tanaka S. Shirak University College London London UK University College London London USA Warwick Medical School London UK Warwick Medical School Coventry USA Radboud University Nijmegen Nijmegen Netherlands Department of Neurology University of Chile Santiago Chile Neurology Service Hospital del Salvador Santiago Chile Family Health Center Juan Pablo II Santiago Chile Department of Biomedicine Section on Physiology Univeristy of Bergen Bergen Norway Department of Public Health and Primary Health Care Univeristy of Bergen Bergen Norway Department of Public Health and Primary Health Care University of Bergen Bergen Norway Norwegian Competence Center for Sleep Disorders Haukeland University Hospital Bergen Norway Department of Child and Adolescent Psychiatry Haukeland University Hospital Univeristy of Bergen Bergen Norway Department of Biomedicine University of Bergen Bergen Norway US Army Medical Research and Materiel Command Fort Detrick Frederick USA Walter Reed Army Institute of Research Silver Spring USA Department of Behavioral Biology Walter Reed Army Institute of Research Silver Spring USA Faculty Department of Pulmonology Pamukkale University Medical Denizli Turkey University of Ballarat Ballarat Australia Sanofi-Aventis Recherche et Developpement Paris France Brigham and Women’s Hospital Harvard Medical School Boston USA The University of Queensland Adelaide Australia School of Psychology University of Queensland Brisbane Australia The University of Queensland Brisbane Australia Victoria University Melbourne Australia Department of Psychology Victoria University Melbourne Australia School of Psychology Victoria University Melbourne Australia Philips-Universität Marburg Germany Philips-Universitaet-Marburg Marburg Germany Instituto de Investigaciones del Sueno Madrid Spain Universitätsklinik für Neurologie Innsbruck Austria Universitaetsklinik fuer Neurologie Innsbruck Austria IMEREM GmbH Nuernberg Germany Schwarz Pharma AG Monheim Germany

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Improved mixed language speech recognition using asymmetric acoustic model and language model with code-switch inversion constraints

Improved mixed language speech recognition using asymmetric ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ying Li Pascale Fung Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781479903573

We propose an integrated framework for large vocabulary continuous mixed language speech recognition that handles the accent effect in the bilingual acoustic model and the inversion constraint well known to linguists in the language model. Our asymmetric acoustic model with phone set extension improves upon previous work by striking a balance between data and phonetic knowledge. Our language model improves upon previous work by (1) using the inversion constraint to predict code switching points in the mixed language and (2) integrating a code-switch prediction model, a translation model and a reconstruction model together. This integration means that our language model avoids the pitfall of propagated error that could arise from decoupling these steps. Finally, a WFST-based decoder integrates the acoustic models, code-switch language model and a monolingual language model in the matrix language all together. Our system reduces word error rate by 1.88% on a lecture speech corpus and by 2.43% on a lunch conversation corpus, with statistical significance, over the conventional bilingual acoustic model and interpolated language model.

关键词： Speech Acoustics Adaptation models Predictive models Speech recognition Data models Hidden Markov models

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SHREC'13 Track: Retrieval of objects captured with low-cost depth-sensing cameras 6

SHREC'13 Track: Retrieval of objects captured with low-cost ...

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6th Eurographics Workshop on 3D Object Retrieval, 3DOR 2013

作者： Machado, J. Ferreira, A. Pascoal, P.B. Abdelrahman, M. Aono, M. El-Melegy, M. Farag, A. Johan, H. Li, B. Lu, Y. Tatsuma, A. INESC-ID IST Technical University of Lisbon Portugal Microsoft Language Development Center Portugal Toyohashi University of Technology Japan Department of Computer Science Texas State University San Marcos United States Fraunhofer IDM at NTU Singapore Singapore Computer Vision and Image Processing Laboratory University of Louisville Louisville United States

ISBN: (纸本)9783905674446

The SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras is a first attempt at evaluating the effectiveness of 3D shape retrieval algorithms in low fidelity model databases, such as the ones captured with commodity depth cameras. Both target and query set are composed by objects captured with a Kinect camera and the objective is to retrieve the models in the target set who were considered relevant by a human-generated ground truth. Given how widespread such devices are, and how easy it is becoming for an everyday user to capture models in his household, the necessity of algorithms for these new types of 3D models is also increasing. Three groups have participated in the contest, providing rank lists for the set of queries, which is composed of 12 models from the target set. © The Eurographics Association 2013.

关键词： Cameras

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Methodological approach from the Best Overall Team in the sbv IMPROVER Diagnostic Signature Challenge

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Systems Biomedicine 2013年第4期1卷 217-227页

作者： Adi L Tarca* Nandor Gabor Than Roberto Romero Perinatology Research Branch The Eunice Kennedy Shriver National Institute of Child Health Human Development (NIchD)/National Institutes of Health (NIh)/United States Department of health and Human Services (Dhhs) Department of Computer Science Wayne

The sbv IMPROVER Diagnostic Signature Challenge used crowdsourcing to identify the best methods to classify clinical samples using transcriptomics data. Participating teams used public microarray data sets to develop prediction models in four disease areas, and then made predictions on blinded test data generated by the organizers. Here we describe the approach of the team for the Perinatology Research Branch (TeamPRB; AL Tarca, R Romero), that was awarded the best performing entrant prize out of 54 entrants. The key elements of our approach included: (1) selection of training data sets by trial and error; (2) removal of batch effects by pre-processing the test and training data together; (3) the use of statistical significance and magnitude of change to select biomarkers; and (4) optimization of the number of biomarkers via the cross-validated performance of a simple linear discriminant analysis (LDA) model. Not only were our resulting models ranked consistently high, but they also generated parsimonious signatures of as low as two genes, unlike most of the other top-ranked teams that used hundreds of genes for prediction.

关键词： microarrays outcome prediction linear discriminant analysis systems biology verification transcriptomics

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