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arXiv 2020年

作者： Feng, Siyuan Zelasko, Piotr Moro-Velázquez, Laureano Abavisani, Ali Hasegawa-Johnson, Mark Scharenborg, Odette Dehak, Najim Multimedia Computing Group Delft University of Technology Delft Netherlands Center for Language and Speech Processing United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States

The idea of combining multiple languages’ recordings to train a single automatic speech recognition (ASR) model brings the promise of the emergence of universal speech representation. Recently, a Transformer encoder-decoder model has been shown to leverage multilingual data well in IPA transcriptions of languages presented during training. However, the representations it learned were not successful in zero-shot transfer to unseen languages. Because that model lacks an explicit factorization of the acoustic model (AM) and language model (LM), it is unclear to what degree the performance suffered from differences in pronunciation or the mismatch in phonotactics. To gain more insight into the factors limiting zero-shot ASR transfer, we replace the encoder-decoder with a hybrid ASR system consisting of a separate AM and LM. Then, we perform an extensive evaluation of monolingual, multilingual, and crosslingual (zero-shot) acoustic and language models on a set of 13 phonetically diverse languages. We show that the gain from modeling crosslingual phonotactics is limited, and imposing a too strong model can hurt the zero-shot transfer. Furthermore, we find that a multilingual LM hurts a multilingual ASR system’s performance, and retaining only the target language’s phonotactic data in LM training is preferable. Copyright © 2020, The Authors. All rights reserved.

关键词： speech recognition

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speech enhancement via deep spectrum image translation network

arXiv

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arXiv 2019年

作者： Kashani, Hamidreza Baradaran Goodarzi, Mohammad Mohsen Jodeiri, Ata Rezaei, Iman Sarraf Electrical Engineering Faculty Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Buein Zahra Technical University Qazvin Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

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 perceptually-modified 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. Copyright © 2019, The Authors. All rights reserved.

关键词： Audition

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speech Enhancement via Deep Spectrum Image Translation Network

Speech Enhancement via Deep Spectrum Image Translation Netwo...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Hamidreza Baradaran Kashani Ata Jodeiri Mohammad Mohsen Goodarzi Iman Sarraf Rezaei Electrical Engineering Faculty Amirkabir University of Technology Tehran Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering Buein Zahra Technical University Qazvin Iran Research Center for Development of Advanced Technologies Speech and Language Processing Group Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156644

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.

关键词： speech enhancement Noise measurement Feature extraction Training computer architecture Convolution Frequency-domain analysis

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JOINT VERIFICATION-IDENTIFICATION IN END-TO-END MULTI-SCALE CNN FRAMEWORK FOR TOPIC IDENTIFICATION

JOINT VERIFICATION-IDENTIFICATION IN END-TO-END MULTI-SCALE ...

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IEEE International Conference on Acoustics, speech and Signal processing

作者： Raghavendra Pappagari Jesús Villalba Najim Dehak Center for Language and Speech Processing Department of Electrical and Computer Engineering The Johns Hopkins University USA

ISBN: (纸本)9781538646595

We present an end-to-end multi-scale Convolutional Neural Network (CNN) framework for topic identification (topic ID). In this work, we examined multi-scale CNN for classification using raw text input. Topical word embeddings are learnt at multiple scales using parallel convolutional layers. A technique to integrate verification and identification objectives is examined to improve topic ID performance. With this approach, we achieved significant improvement in identification task. We evaluated our framework on two contrasting datasets: 20 newsgroups and Fisher. We obtained 92.93% accuracy on Fisher and 86.12% on 20 newsgroups, which to our knowledge are the best published results on these datasets at the moment.

关键词： BOW raw text CNN verification identification topic id end-to-end BOW end-to-end Verification Martes pennanti newsgroups multi-scale Joints Dataset

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A potential neurophysiological correlate of electric-acoustic pitch matching in adult cochlear implant users: Pilot data

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Cochlear Implants International 2018年第4期19卷 198-209页

作者： Tan, Chin-Tuan Martin, Brett A. Svirsky, Mario A. Department of Electrical and Computer Engineering School of Behavioral and Brain Science (Callier Center for Communication Disorders) University of Texas at Dallas Richardson TX United States Program in Speech-Language-Hearing Sciences and Program in Audiology Graduate Center City University of New York New York NY United States Department of Otolaryngology New York University New York NY United States

The overall goal of this study was to identify an objective physiological correlate of electric-acoustic pitch matching in unilaterally implanted cochlear implant (CI) participants with residual hearing in the non-implanted ear. electrical and acoustic stimuli were presented in a continuously alternating fashion across ears. The acoustic stimulus and the electrical stimulus were either matched or mismatched in pitch. Auditory evoked potentials were obtained from nine CI users. Results indicated that N1 latency was stimulus-dependent, decreasing when the acoustic frequency of the tone presented to the non-implanted ear was increased. More importantly, there was an additional decrease in N1 latency in the pitch-matched condition. These results indicate the potential utility of N1 latency as an index of pitch matching in CI users. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Auditory evoked potential Electric-acoustic pitch matching N1 latency

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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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

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Enhancement and Analysis of Conversational speech: JSALT 2017

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

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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

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

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

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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

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Neuromorphic sampling on the SpiNNaker and parallella chip multiprocessors 7

Neuromorphic sampling on the SpiNNaker and parallella chip m...

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7th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2016

作者： Mendat, Daniel R. Chin, Sang Furber, Steve Andreou, Andreas G. Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States School of Computer Science University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9781467378352

We present a bio-inspired, hardware/software architecture to perform Markov Chain Monte Carlo sampling on probabilistic graphical models using energy aware hardware. We have developed algorithms and programming data flows for two recently developed multiprocessor architectures, the SpiNNaker and Parallella. We employ a neurally inspired sampling algorithm that abstracts the functionality of neurons in a biological network and exploits the neural dynamics to implement the sampling process. This algorithm maps nicely on the two hardware systems. Speedups as high as 1000 fold are achieved when performing inference using this approach, compared to algorithms running on traditional engineering workstations. © 2016 IEEE.

关键词： Markov processes

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