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arXiv 2022年

作者： Daheim, Nico Thulke, David Dugast, Christian Ney, Hermann Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Human Language Technology and Pattern Recognition RWTH Aachen University Germany AppTek GmbH Germany

In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes' theorem. One component is a traditional ungrounded response generation model and the other component models the reconstruction of the grounding document based on the dialog context and generated response. We propose different approximate decoding schemes and evaluate our approach on multiple open-domain and task-oriented document-grounded dialog datasets. Our experiments show that the model is more factual in terms of automatic factuality metrics than the baseline model. Furthermore, we outline how introducing scaling factors between the components allows for controlling the tradeoff between factuality and fluency in the model output. Finally, we compare our approach to a recently proposed method to control factuality in grounded dialog, CTRL (Rashkin et al., 2021), and show that both approaches can be combined to achieve additional improvements. © 2022, CC BY.

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A comparative large scale study of MLP features for mandarin ASR

A comparative large scale study of MLP features for mandarin...

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作者： Valente, Fabio Doss, Mathew Magimai Plahl, Christian Ravuri, Suman Wang, Wen IDIAP Research Institute CH-1920 Martigny Switzerland Human Language Technology and Pattern Recognition RWTH Aachen University Germany International Computer Science Institute 1947 Center Street Berkeley CA 94704 United States Speech Technology and Research Laboratory SRI International Menlo Park CA United States

MLP based front-ends have shown significant complementary properties to conventional spectral features. As part of the DARPA GALE program, different MLP features were developed for Mandarin ASR. In this paper, all the proposed frontends are compared in systematic manner and we extensively investigate the scalability of these features in terms of the amount of training data (from 100 hours to 1600 hours) and system complexity (maximum likelihood training, SAT, lattice level combination, and discriminative training). Results on 5 hours of evaluation data from the GALE project reveal that the MLP features consistently produce relative improvements in the range of 15% - 23% at the different steps of a multipass system when compared to the conventional short-term spectral based features like MFCC and PLP. The largest improvement is obtained using a hierarchical MLP approach. © 2010 ISCA.

关键词： Maximum likelihood

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RADMM: RECURRENT ADAPTIVE MIXTURE MODEL WITH APPLICATIONS TO DOMAIN ROBUST language MODELING

RADMM: RECURRENT ADAPTIVE MIXTURE MODEL WITH APPLICATIONS TO...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kazuki Irie Shankar Kumar Michael Nirschl Hank Liao Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University D-52056 Aachen Germany Google Inc. New York NY 10011 USA

ISBN: (纸本)9781538646595

We present a new architecture and a training strategy for an adaptive mixture of experts with applications to domain robust language modeling. The proposed model is designed to benefit from the scenario where the training data are available in diverse domains as is the case for YouTube speech recognition. The two core components of our model are an ensemble of parallel long short-term memory (LSTM) expert layers for each domain and another LSTM based network which generates state dependent mixture weights for combining expert LSTM states by linear interpolation. The resulting model is a recurrent adaptive mixture model (RADMM) of domain experts. We train our model on 4.4B words from YouTube speech recognition data. We report results on the YouTube speech recognition test set. Compared with a background LSTM model, we obtain up to 12% relative improvement in perplexity and an improvement in word error rate from 12.3% to 12.1% while using a lattice rescoring with strong pruning.

关键词： language modeling neural networks speech recognition mixture of experts domain adaptation modelling languages Speech recognition Neural network Professional personnel YouTube Mixture models training policy new buildings

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Complementary fusion of multi-features and multi-modalities in sentiment analysis

arXiv

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arXiv 2019年

作者： Chen, Feiyang Luo, Ziqian Xu, Yanyan Ke, Dengfeng Department of Computer Science and Technology Beijing Forestry University School of Computer Science Language Technologies Institute Carnegie Mellon University National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

Sentiment analysis, mostly based on text, has been rapidly developing in the last decade and has attracted widespread attention in both academia and industry. However, information in the real world usually comes from multiple modalities, such as audio and text. Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis. We call it the DFF-ATMF (Deep Feature Fusion - Audio and Text Modality Fusion) model, which consists of two parallel branches, the audio modality based branch and the text modality based branch. Its core mechanisms are the fusion of multiple feature vectors and multiple modality attention. Experiments on the CMU-MOSI dataset and the recently released CMUMOSEI dataset, both collected from YouTube for sentiment analysis, show the very competitive results of our DFF-ATMF model. Furthermore, by virtue of attention weight distribution heatmaps, we also demonstrate the deep features learned by using DFF-ATMF are complementary to each other and robust. Surprisingly, DFF-ATMF also achieves new state-ofthe-art results on the IEMOCAP dataset, indicating that the proposed fusion strategy also has a good generalization ability for multimodal emotion recognition. Copyright © 2019, The Authors. All rights reserved.

关键词： Sentiment analysis

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Mobile music modeling, analysis and recognition

Mobile music modeling, analysis and recognition

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

作者： Pavel Golik Boulos Harb Ananya Misra Michael Riley Alex Rudnick Eugene Weinstein Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Google Inc. New York NY USA School of Informatics and Computing Indiana University Bloomington IN USA

We present an analysis of music modeling and recognition techniques in the context of mobile music matching, substantially improving on the techniques presented in [1]. We accomplish this by adapting the features specifically to this task, and by introducing new modeling techniques that enable using a corpus of noisy and channel-distorted data to improve mobile music recognition quality. We report the results of an extensive empirical investigation of the system's robustness under realistic channel effects and distortions. We show an improvement of recognition accuracy by explicit duration modeling of music phonemes and by integrating the expected noise environment into the training process. Finally, we propose the use of frame-to-phoneme alignment for high-level structure analysis of polyphonic music.

关键词： Training Accuracy Hidden Markov models Music Speech recognition USA Councils

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On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting recognition

On the Benefits of Convolutional Neural Network Combinations...

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International Workshop on Frontiers in Handwriting recognition

作者： Dewi Suryani Patrick Doetsch Hermann Ney King Mongkut's University of Technology North Bangkok The Sirindhorn International Thai-German Graduate School of Engineering Bangkok Thailand Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781509009824

In this paper, we elaborate the advantages of combining two neural network methodologies, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, with the framework of hybrid hidden Markov models (HMM) for recognizing offline handwriting text. CNNs employ shift-invariant filters to generate discriminative features within neural networks. We show that CNNs are powerful tools to extract general purpose features that even work well for unknown classes. We evaluate our system on a Chinese handwritten text database and provide a GPU-based implementation that can be used to reproduce the experiments. All experiments were conducted with RWTH OCR, an open-source system developed at our institute.

关键词： Hidden Markov models Neurons Training Handwriting recognition Biological neural networks computer architecture Logic gates

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

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关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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An evolutionary lion optimization algorithm-based image compression technique for biomedical applications

An evolutionary lion optimization algorithm-based image comp...

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作者： Geetha, Karuppaiah Anitha, Veerasamy Elhoseny, Mohamed Kathiresan, Shankar Shamsolmoali, Pourya Selim, Mahmoud M. Department of Computer Science & Engineering MIET Engineering College Tamilnadu India Department of Computer Science and Engineering Imayam College of Engineering Anna University ChennaiTamilnadu India Faculty of Computers and Information Mansoura University Mansoura Egypt Scientific Group for Research and Technology Cairo Egypt Department of Computer Applications Alagappa University Karaikudi India Huawei Technologies Corporate Shanghai China. & Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Mathematics Al-Aflaj College of Science and Human Studies Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image. © 2020 John Wiley & Sons Australia, Ltd

关键词： Vector quantization

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Bridging the Native language and language Variety Identification Tasks

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Procedia computer science 2017年 112卷 1554-1561页

作者： Marc Franco-Salvador Greg Kondrak Paolo Rosso Symanto Research 90425 Nuremberg Germany Pattern Recognition and Human Language Technology (PRHLT) Research Center Universitat Politècnica de València 46022 Valencia Spain Department of Computing Science University of Alberta Edmonton AB T6G 2E8 Canada

The objective of Native language Identification is to determine the native language of the author of a text that he or she wrote in another language. By contrast, language Variety Identification aims at classifying texts representing different varieties of a single language. We postulate that both tasks may be reduced to a single objective, which is to identify the language variety of the text. We design a general approach that combines string kernels and word embeddings, which capture different characteristics of texts. The results of our experiments show that the approach achieves excellent results on both tasks, without any task-specific adaptations.

关键词： Native language Identification language Variety Identification String Kernels Word Embeddings Classifier Combination

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