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第十四届全国人机语音通讯学术会议

作者： Kaiyu Shi Xuan Liu Yanmin Qian Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

Speech emotion recognition is one of the latest challenges in speech processing,we implemented a hybrid system for speech emotion *** methods were proposed,compared and *** first,we utilized GMM-HMM model fed with MFCC feature to exploit the dynamics of emotional *** a balanced SVM classifier was applied on the static LLD *** order to take advantage of generative model and discriminate model,these two methods were combined by taking the probability representation of the GMM-HMM model as the additional features for the SVM *** hybrid system makes full use of both MFCC feature and LLD feature,the performance of the hybrid system also proves the effectiveness of our proposed system.

关键词： speech emotion recognition SVM GMMHMM hybrid system

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Emotion Recognition Using Support Vector Machine and Deep Neural Network

Emotion Recognition Using Support Vector Machine and Deep Ne...

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第十四届全国人机语音通讯学术会议

作者： Chen Ruinian Zhou Ying Qian Yanmin Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

We propose a classification system which is a combination of three subsystem to solve the speech emotion recognition *** first subsystem utilizes support vector machines(SVMs) to classify the features *** second subsystem utilizes deep neural networks(DNNs) to classify the features *** the third subsystem,we utilize DNNs to extract high level features from raw data and show that they are effective for speech emotion *** first produce an emotion state probability distribution for each speech segment using DNNs,then we construct utterance-level features from segment-level probability *** utterance level features are fed into an SVM to identify utterance-level *** experimental results show that all the subsystems are outperform the HMM baseline system,and the combined system get the best result on F-score.

关键词： Emotion recognition Deep neural networks Support vector machine

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Fusion Model for Speech Emotion Recognition with Low Level Descriptor Features

Fusion Model for Speech Emotion Recognition with Low Level D...

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第十四届全国人机语音通讯学术会议

作者： Cheng Chang Huifeng Zhang Zhangxuan Gu Yanmin Qian Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

Speech emotion recognition is one of the most challenging speech processing tasks,with many applications in the field of Human-Machine Interaction(HMI).Traditional works have been using Gaussian Mixture Models(GMM) for *** recent researches,Deep Neural Networks(DNN) have shown strong ability in feature learning and modeling in many *** this paper,we present to utilize the DNN to learn extra features from Low Level Descriptor(LLD) features for other classifiers,and propose a decision-level fusion model for speech emotion *** carry out experiments and evaluations on a public German corpus called FAU-AIBO with five *** our experimental results using LLD features demonstrate that our proposed approach improves the recognition performance on unweighted results and outperforms the baseline using GMM significantly.

关键词： speech emotion recognition deep neural network low level descriptor fusion

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SMALL-FOOTPRINT CONVOLUTIONAL NEURAL NETWORK FOR SPOOFING DETECTION

SMALL-FOOTPRINT CONVOLUTIONAL NEURAL NETWORK FOR SPOOFING DE...

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International Joint Conference on Neural Networks

作者： Heinrich Dinkel Yanmin Qian Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

ISBN: (纸本)9781509061839

Albeit recent progress in speaker verification engendered powerful models, malicious attacks in the form of spoofed speech, are generally not coped with. In previous attempts, deep neural networks were used to extract high dimensional features which were later classified using an independent classifier. Even though the results of this approach are promising, this architecture's disadvantage is it's complexity of optimizing both, neural network and back-end classifier. In this paper we present a simplified neural network approach to address this problem based on the convolutional neural network architecture. Our model concatenates the output of all abstract convolutional representations within the network into a single high-dimensional vector. By preserving all the information within the network, the networks generalization capabilities are greatly enhanced, resulting in an favorable error rate of 5.4% on the S10 condition. Scores are frame wise obtained by directly extracting the posteriors from the output neurons and further reduced to an utterance score by the use of variance reduction. We show that by using variance posterior score reduction, large performance gains can be achieved. This model outperforms standard feature extracting neural network approaches, in addition on being more versatile, robust and faster to train. Our best model achieves an error rate of 0.7% on the ASVspoof corpus, utilizing common PLP features. It significantly outperforms conventional feature extraction neural networks, while only having 100k parameters.

关键词： Neural network Error analysis Feature extraction speaker recognition Classifiers Information Network neural net architecture

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Understanding patch-based learning by explaining predictions

arXiv

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arXiv 2018年

作者： Anders, Christopher Montavon, Grégoire Samek, Wojciech Müller, Klaus-Robert Dept. of Computer Science Technische Universität Berlin Berlin10587 Germany Dept. of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Dept. of Brain & Cognitive Engineering Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Saarbrücken66123 Germany

Deep networks are able to learn highly predictive models of video data. Due to video length, a common strategy is to train them on small video snippets. We apply the deep Taylor / LRP technique to understand the deep network's classification decisions, and identify a "border effect": a tendency of the classifier to look mainly at the bordering frames of the input. This effect relates to the step size used to build the video snippet, which we can then tune in order to improve the classifier's accuracy without retraining the model. To our knowledge, this is the the first work to apply the deep Taylor / LRP technique on any video analyzing neural network. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Encoder-decoder with focus-mechanism for sequence labelling based spoken language understanding

Encoder-decoder with focus-mechanism for sequence labelling ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Su Zhu Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University China

ISBN: (纸本)9781509041183

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

关键词： Spoken language understanding encoder-decoder focus-mechanism robustness spoken language understanding Word Packaging Short-Term Memory Robustness Speech recognition

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Author Correction: Aplp1 interacts with Lag3 to facilitate transmission of pathologic α-synuclein

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Nature communications 2024年第1期15卷 6413页

作者： Xiaobo Mao Hao Gu Donghoon Kim Yasuyoshi Kimura Ning Wang Enquan Xu Ramhari Kumbhar Xiaotian Ming Haibo Wang Chan Chen Shengnan Zhang Chunyu Jia Yuqing Liu Hetao Bian Senthilkumar S Karuppagounder Fatih Akkentli Qi Chen Longgang Jia Heehong Hwang Su Hyun Lee Xiyu Ke Michael Chang Amanda Li Jun Yang Cyrus Rastegar Manjari Sriparna Preston Ge Saurav Brahmachari Sangjune Kim Shu Zhang Yasushi Shimoda Martina Saar Haiqing Liu Sin Ho Kweon Mingyao Ying Creg J Workman Dario A A Vignali Ulrike C Muller Cong Liu Han Seok Ko Valina L Dawson Ted M Dawson Neuroregeneration and Stem Cell Programs Institute for Cell Engineering Johns Hopkins University School of Medicine Baltimore MD 21205 USA. xmao4@jhmi.edu. Department of Neurology Johns Hopkins University School of Medicine Baltimore MD 21205 USA. xmao4@jhmi.edu. Adrienne Helis Malvin Medical Research Foundation New Orleans LA 70130-2685 USA. xmao4@jhmi.edu. Neuroregeneration and Stem Cell Programs Institute for Cell Engineering Johns Hopkins University School of Medicine Baltimore MD 21205 USA. Department of Neurology Johns Hopkins University School of Medicine Baltimore MD 21205 USA. Department of Neurology Nanjing Brain Hospital Nanjing Jiangsu 210029 PR China. Medical College Yangzhou University Yangzhou Jiangsu 225001 PR China. Department of Pharmacology College of Medicine Dong-A University 32 Daesin Gongwwon-ro Seo-gu Busan 49201 Republic of Korea. Adrienne Helis Malvin Medical Research Foundation New Orleans LA 70130-2685 USA. Department of Anesthesiology West China Hospital Sichuan University. The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences West China Hospital Sichuan University Chengdu Sichuan 610041 PR China. Interdisciplinary Research Center on Biology and Chemistry Shanghai Institute of Organic Chemistry Chinese Academy of Sciences 26 Qiuyue Road Shanghai 201210 China. University of the Chinese Academy of Sciences 19 A Yuquan Road Shijingshan District Beijing 100049 China. Institute for NanoBioTechnology Johns Hopkins University Baltimore MD 21218 USA. Department of Materials Science and Engineering Whiting School of Engineering Johns Hopkins University Baltimore MD 21218 USA. Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA. Picower Institute for Learning and Memory Cambridge MA 02139 USA. Harvard-MIT MD/PhD Program Harvard Medical School Boston MA 02115 USA. Department of Biological Science and Biotechnology Chungbuk Nati

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Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being

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Music and science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

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Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure

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Biological imaging 2023年 3卷 e11页

作者： Eduardo A González-Solares Ali Dariush Carlos González-Fernández Aybüke Küpcü Yoldaş Alireza Molaeinezhad Mohammad Al Sa'd Leigh Smith Tristan Whitmarsh Neil Millar Nicholas Chornay Ilaria Falciatori Atefeh Fatemi Daniel Goodwin Laura Kuett Claire M Mulvey Marta Páez Ribes Fatime Qosaj Andrew Roth Ignacio Vázquez-García Spencer S Watson Jonas Windhager Samuel Aparicio Bernd Bodenmiller Ed Boyden Carlos Caldas Owen Harris Sohrab P Shah Simon Tavaré Dario Bressan Gregory J Hannon Nicholas A Walton Institute of Astronomy University of Cambridge Cambridge United Kingdom. CRUK Cambridge Institute Li Ka Shing Centre University of Cambridge Cambridge United Kingdom. McGovern Institute Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA. McGovern Institute Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA. Department of Quantitative Biomedicine University of Zurich Zurich Switzerland. Institute of Molecular Life Sciences University of Zurich Zurich Switzerland. Department of Computer Science University of British Columbia Vancouver BC Canada. Herbert and Florence Irving Institute for Cancer Dynamics Columbia University New York NY USA. Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA. Department of Oncology and Ludwig Institute for Cancer Research University of Lausanne Lausanne Switzerland. Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada. Howard Hughes Medical Institute Department of Physics Harvard University Cambridge MA USA. Howard Hughes Medical Institute Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA. Cambridge Breast Unit Addenbrooke's Hospital Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre Cambridge United Kingdom. Súil Interactive Dublin Ireland. New York Genome Center New York NY USA.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

关键词： Data management data processing and analysis fluorescence microscopy imaging mass cytometry

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Concept transfer learning for adaptive language understanding

arXiv

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arXiv 2017年

作者： Zhu, Su Yu, Kai Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Semantic transfer is an important problem of the language understanding (LU), which is about how the recognition pattern of a semantic concept benefits other associated concepts. In this paper, we propose a new semantic representation based on combinatory concepts. Semantic slot is represented as a composition of different atomic concepts in different semantic dimensions. Specifically, we propose the concept transfer learning methods for extending combinatory concepts in LU. The concept transfer learning makes use of the common ground of combinatory concepts shown in the literal description. Our methods are applied to two adaptive LU problems: semantic slot refinement and domain adaptation, and respectively evaluated on two benchmark LU datasets: ATIS and DSTC 2&3. The experiment results show that the concept transfer learning is very efficient for semantic slot refinement and domain adaptation in the LU. Copyright © 2017, The Authors. All rights reserved.

关键词： Semantics

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