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Exploring the robustness of features and enhancement on speech recognition systems in highly-reverberant real environments

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arXiv 2018年

作者： Novoa, José Escudero, Juan Pablo Wuth, Jorge Poblete, Victor King, Simon Stern, Richard Yoma, Néstor Becerra Electrical Engineering Department Speech Processing and Transmission Laboratory Universidad de Chile Santiago Chile Institute of Acoustics Universidad Austral de Chile Valdivia Chile Centre for Speech Technology Research University of Edinburgh Edinburgh United Kingdom Department of Electrical and Computer Engineering and Language Technologies Institute Carnegie Mellon University Pittsburgh United States

This paper evaluates the robustness of a DNN-HMM-based speech recognition system in highly-reverberant real environments using the HRRE database. The performance of locally-normalized filter bank (LNFB) and Mel filter bank (MelFB) features in combination with Non-negative Matrix Factorization (NMF), Suppression of Slowly-varying components and the Falling edge (SSF) and Weighted Prediction Error (WPE) enhancement methods are discussed and evaluated. Two training conditions were considered: Clean and reverberated (Reverb). With Reverb training the use of WPE and LNFB provides WERs that are 3% and 20% lower in average than SSF and NMF, respectively. WPE and MelFB provides WERs that are 11% and 24% lower in average than SSF and NMF, respectively. With clean training, which represents a significant mismatch between testing and training conditions, LNFB features clearly outperform MelFB features. The results show that different types of training, parametrization, and enhancement techniques may work better for a specific combination of speaker-microphone distance and reverberation time. This suggests that there could be some degree of complementarity between systems trained with different enhancement and parametrization methods. Index Terms: speech recognition, enhancement techniques, reverberation, real environments. Copyright © 2018, The Authors. All rights reserved.

关键词： Reverberation

Common Limitations of Image processing Metrics: A Picture Story

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

PRIF: A Privacy-Preserving Interest-Based Forwarding Scheme for Social Internet of Vehicles Liehuang Zhu, Chuan Zhang, Chang Xu, Rixin Xu, Kashif Sharif are with

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arXiv 2018年

作者： Zhu, Liehuang Zhang, Chuan Xu, Chang Du, Xiaojiang Xu, Rixin Sharif, Kashif Guizani, Mohsen Beijing Eng. Res. Center of Massive Language Information Processing and Cloud Computing Application Beijing Institute of Technology School of Computer Science and Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho Moscow Idaho United States

Recent advances in Socially Aware Networks (SANs) have allowed its use in many domains, out of which social Internet of vehicles (SIOV) is of prime importance. SANs can provide a promising routing and forwarding paradigm for SIOV by using interest-based communication. Though able to improve the forwarding performance, existing interest-based schemes fail to consider the important issue of protecting users' interest information. In this paper, we propose a PRivacy-preserving Interest-based Forwarding scheme (PRIF) for SIOV, which not only protects the interest information, but also improves the forwarding performance. We propose a privacy-preserving authentication protocol to recognize communities among mobile nodes. During data routing and forwarding, a node can know others' interests only if they are affiliated with the same community. Moreover, to improve forwarding performance, a new metric community energy is introduced to indicate vehicular social proximity. Community energy is generated when two nodes encounter one another and information is shared among them. PRIF considers this energy metric to select forwarders towards the destination node or the destination community. Security analysis indicates PRIF can protect nodes' interest information. In addition, extensive simulations have been conducted to demonstrate that PRIF outperforms the existing algorithms including the BEEINFO, Epidemic, and PRoPHET. Copyright © 2018, The Authors. All rights reserved.

关键词： Vehicles

LPTD: Achieving lightweight and privacy-preserving truth discovery in CIoT

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arXiv 2018年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Sharif, Kashif Du, Xiaojiang Guizani, Mohsen Beijing Eng. Research Center of Massive Language Information Processing and Cloud Computing App. School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and oneway hash chain techniques. This scheme not only protects devices' privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes. Copyright © 2018, The Authors. All rights reserved.

关键词： Internet of things

PPLS: A privacy-preserving location-sharing scheme in vehicular social networks

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arXiv 2018年

作者： Xu, Chang Xie, Xuan Zhu, Liehuang Sharif, Kashif Zhang, Chuan Du, Xiaojiang Guizani, Mohsen Beijing Eng. Res. Center of Massive Language Information Processing and Cloud Computing Application Beijing Institute of Technology School of Computer Science and Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

The recent proliferation of mobile computing has given rise to vehicular so-cial networks (VSNs) which use the Internet of Vehicles and social networks as the main design elements. As one of the most critical components in VSNs, location sharing plays an important role in helping vehicles share information and strengthen their social bonds. This, however, may compromise vehicles' privacy, including location information and social relationship details. Some solutions have been proposed to address these challenges. However, none of them considers privacy of inter-user threshold distance, which e_ectively can be used to identify vehicles, their friends, and location information, by mali-cious or undesired elements of the system. In order to overcome this limitation, we propose a secure distance comparison protocol. Furthermore, we present a privacy-preserving location-sharing scheme in VSNs, namely PPLS, which al-lows vehicles to build more complex access control policies. The safety of our scheme is validated by the security analysis, and experimental results demon-strate the eficiency of PPLS scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Vehicles

Combing context and commonsense knowledge through neural networks for solving winograd schema problems

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Combing context and commonsense knowledge through neural net...

2017 AAAI Spring Symposium

作者： Liu, Quan Jiang, Hui Ling, Zhen-Hua Zhu, Xiaodan Wei, Si Hu, Yu National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui China Department of Electrical Engineering and Computer Science York University Canada National Research Council Canada Ottawa Canada IFLYTEK Research Hefei China

ISBN: (纸本)9781577357797

This paper proposes a general framework to combine context and commonsense knowledge for solving the Winograd Schema (WS) and Pronoun Disambiguation Problems (PDP). In the proposed framework, commonsense knowledge bases (e.g. cause-effect word pairs) are quantized as knowledge constraints. The constraints guide us to learn knowledge enhanced embeddings (KEE) from large text corpus. Based on the pre-trained KEE models, this paper proposes two methods to solve the WS and PDP problems. The first method is an unsupervised method, which represents all the pronouns and candidate mentions in continuous vector spaces based on their contexts and calculates the semantic similarities between all the possible word pairs. The pronoun disambiguation procedure could then be implemented by comparing the semantic similarities between the pronoun (to be resolved) and all the candidate mentions. The second method is a supervised method, which extracts features for all the pronouns and candidate mentions and solves the WS problems by training a typical mention pair classification model. Similar to the first method, the features used in the second method are also extracted based on the KEE models. Experiments conducted on the available PDP and WS test sets show that, these two methods both achieve consistent improvements over the baseline systems. The best performance reaches 62% in accuracy on the PDP test set of the first Winograd Schema Challenge. © Copyright 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Vector spaces

A novel modular wireless sensor networks approach for security applications

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International Journal of Security and Networks 2017年第1期12卷 40-50页

作者： Charalampidou, Maria Pavlidis, George Mouroutsos, Spyridon G. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67-100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67-100 Greece

Nowadays surveillance systems are becoming increasingly complex by combining a variety of sensors and systems in order to deliver more accurate decisions. This is due to the fact that the development of a simple and yet efficient algorithm for intruder detection is of a high interest both to academia and the market. This paper presents a novel approach towards the development of a sophisticated decision-making system for accurate intrusion detection. The method is based on three key elements: the assessment of the certainty of the inputs, the quantisation of the inputs using the three-valued logic and the time-based filtering of the sequence of alarms. The algorithm was applied to a wireless sensor network (WSN) that is used for intruder detection of an open area. The overall setup, the theoretical analysis and preliminary evaluation results of the proposed method show that this is an interesting and rather promising approach. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

Enhancement and Analysis of Conversational speech: JSALT 2017

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Enhancement and Analysis of Conversational Speech: JSALT 201...

IEEE International Conference on Acoustics, speech and Signal processing

作者： Neville Ryanta Elika Bergelson Kenneth Church Alejandrina Cristia Jun Du Sriram Ganapathy Sanjeev Khudanpur Diana Kowalski Mahesh Krishnamoorthy Rajat Kulshreshta Mark Liberman Yu-Ding Lu Matthew Maciejewski Florian Metze Jan Profant Lei Sun Yu Tsao Zhou Yu Linguistic Data Consortium University of Pennsylvania Philadelphia PA USA Department of Psychology and Neuroscience Duke University Durham NC USA IBM Yorktown Heights NY USA Laboratoire de Sciences Cognitives et Psycholinguistique ENS Paris France University of Science and Technology of China Hefei China Electrical Engineering Department Indian Institute of Science Bangalore India Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA University of Illinois at Urbana-Champaign Champaign IL USA Apple Cupertino CA USA Language Technologies Institute Carnegie Mellon University Pittsburgh PA USA Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Brno University of Technology Brno Czech Republic Department of Computer Science University of California Davis Davis CA USA

ISBN: (纸本)9781538646595

Automatic speech recognition is more and more widely and effectively used. Nevertheless, in some automatic speech analysis tasks the state of the art is surprisingly poor. One of these is "diarization", the task of determining who spoke when. Diarization is key to processing meeting audio and clinical interviews, extended recordings such as police body cam or child language acquisition data, and any other speech data involving multiple speakers whose voices are not cleanly separated into individual channels. Overlapping speech, environmental noise and suboptimal recording techniques make the problem harder. During the JSALT Summer Workshop at CMU in 2017, an international team of researchers worked on several aspects of this problem, including calibration of the state of the art, detection of overlaps, enhancement of noisy recordings, and classification of shorter speech segments. This paper sketches the workshop's results, and announces plans for a "Diarization Challenge" to encourage further progress.

关键词： diarization overlap detection speech enhancement automatic speech recognition speech recognition speech enhancement speech state of the art monuron Recordings

Context-dependent point process models for keyword search and detection-based ASR

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Context-dependent point process models for keyword search an...

International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Chunxi Liu Aren Jansen Sanjeev Khudanpur Department of Electrical and Computer Engineering Center for Language and Speech Processing Human Language Technology Center of Excellence The Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479999897

The point process model (PPM) for keyword search (KWS) is a whole-word parametric approach that characterizes each query type by the timing of phonetic events observed during its production. In this paper, we first extend the PPM modeling framework to operate on context-dependent phonetic event patterns instead of monophone patterns considered in the past, which provides significant KWS improvements. Second, we use the context-dependent PPMs to drive a detection-based speech recognition architecture thats runs parallel word detectors covering the whole vocabulary and uses the independent detections to construct lattices that can be used for both KWS indexing and LVCSR decoding. This strategy produces significant improvements over the original PPM KWS framework and provides an encouraging first attempt at PPM-based LVCSR.

关键词： Hidden Markov models Lattices Acoustics Indexes Context modeling Vocabulary Computational modeling