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A joint graph inference case study: the C. elegans chemical and electrical connectomes

Worm 2016年第2期5卷 e1142041页

作者： Li Chen Joshua T Vogelstein Vince Lyzinski Carey E Priebe Software and Service Group Intel Corporation Hillsboro OR USA. Department of Biomedical Engineering and Institute for Computational Medicine Johns Hopkins University Baltimore MD USA. Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA.

We investigate joint graph inference for the chemical and electrical connectomes of the Caenorhabditis elegans roundworm. The C. elegans connectomes consist of [Formula: see text] non-isolated neurons with known functional attributes, and there are two types of synaptic connectomes, resulting in a pair of graphs. We formulate our joint graph inference from the perspectives of seeded graph matching and joint vertex classification. Our results suggest that connectomic inference should proceed in the joint space of the two connectomes, which has significant neuroscientific implications.

关键词： fusion inference joint graph inference joint vertex classification neural connectome omnibus embedding seeded graph matching

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SUPERVISED DOMAIN ADAPTATION FOR I-VECTOR BASED SPEAKER RECOGNITION

SUPERVISED DOMAIN ADAPTATION FOR I-VECTOR BASED SPEAKER RECO...

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IEEE International Conference on Acoustics, speech and Signal processing

作者： Daniel Garcia-Romero Alan McCree Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD

ISBN: (纸本)9781479928941

In this paper, we present a comprehensive study on supervised domain adaptation of PLDA based i-vector speaker recognition systems. After describing the system parameters subject to adaptation, we study the impact of their adaptation on recognition performance. Using the recently designed domain adaptation challenge, we observe that the adaptation of the PLDA parameters (i.e. across-class and within-class covariances) produces the largest gains. Nonetheless, length-normalization is also important;whereas using an indomain UBM and T matrix is not crucial. For the PLDA adaptation, we compare four approaches. Three of them are proposed in this work, and a fourth one was previously published. Overall, the four techniques are successful at leveraging varying amounts of labeled in-domain data and their performance is quite similar. However, our approaches are less involved, and two of them are applicable to a larger class of models (low-rank across-class).

关键词： Loudspeakers S matrix system parameter Large scale models speaker recognition

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Facebook, twitter and google plus for breaking news: Is there awinner? 8

Facebook, twitter and google plus for breaking news: Is ther...

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8th International Conference on Weblogs and Social Media, ICWSM 2014

作者： Osborne, Miles Dredze, Mark School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States

ISBN: (纸本)9781577356578

Twitter is widely seen as being the go to place for breaking news. Recently however, competing Social Media have begun to carry news. Here we examine how Facebook, Google Plus and Twitter report on breaking *** consider coverage (whether news events are reported) and latency (the time when they are reported). Using data drawn from three weeks in December 2013, we identify 29 major news events, ranging from celebrity deaths, plague outbreaks to sports events. We find that all media carry the same major events, but Twitter continues to be the preferred medium for breaking news, almost consistently leading Facebook or Google Plus. Facebook and Google Plus largely repost newswire stories and their main research value is that they conveniently package multitple sources of information together.. Copyright © 2014, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Speaker diarization with plda i-vector scoring and unsupervised calibration

Speaker diarization with plda i-vector scoring and unsupervi...

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IEEE Spoken language technology Workshop

作者： Gregory Sell Daniel Garcia-Romero Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

Speaker diarization via unsupervised i-vector clustering has gained popularity in recent years. In this approach, i-vectors are extracted from short clips of speech segmented from a larger multi-speaker conversation and organized into speaker clusters, typically according to their cosine score. In this paper, we propose a system that incorporates probabilistic linear discriminant analysis (PLDA) for i-vector scoring, a method already frequently utilized in speaker recognition tasks, and uses unsupervised calibration of the PLDA scores to determine the clustering stopping criterion. We also demonstrate that denser sampling in the i-vector space with overlapping temporal segments provides a gain in the diarization task. We test our system on the CALLHOME conversational telephone speech corpus, which includes multiple languages and a varying number of speakers, and we show that PLDA scoring outperforms the same system with cosine scoring, and that overlapping segments reduce diarization error rate (DER) as well.

关键词： speech Calibration Density estimation robust algorithm Principal component analysis speech processing Speaker recognition

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AUTOMATIC CARRIER PITCH ESTIMATION FOR COHERENT DEMODULATION

AUTOMATIC CARRIER PITCH ESTIMATION FOR COHERENT DEMODULATION

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IEEE International Conference on Acoustics, speech and Signal processing

作者： Gregory Sell Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479928941

This paper proposes a method for estimating carrier frequencies for coherent demodulation using low-frequency amplitude modulation criteria and a frequency-smoothing regularizer. This process combines with coherent modulator estimation to create an iterative approach for simultaneously determining the optimally low-frequency modulator and corresponding carrier for an arbitrary signal. The method is demonstrated with unsupervised semi-blind source separation of speech mixed with several types of tonal interference.

关键词： coherent demodulation CARRIER FREQUENCY Source separation Demodulation PITCHES pitch Ship motion speech AUTOMATIC Modulators

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MUSIC TONALITY FEATURES FOR speech/MUSIC DISCRIMINATION

MUSIC TONALITY FEATURES FOR SPEECH/MUSIC DISCRIMINATION

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IEEE International Conference on Acoustics, speech and Signal processing

作者： Gregory Sell Pascal Clark Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479928941

We introduce a novel set of features for speech/music discrimination derived from chroma vectors, a feature that represents musical tonality. These features are shown to outperform other commonly used features in multiple conditions and corpora. Even when trained on mismatched data, the new features perform well on their own and also combine with existing features for further improvement. We report 97.1% precision on speech and 93.0% precision on music for the Broadcast News corpus using a simple classifier trained on a mismatched corpus.

关键词： Multiple signal classification MISMATCH Music Discrimination speech Precision WWOX gene Chroma

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Unsupervised clustering approaches for domain adaptation in speaker recognition systems

Unsupervised clustering approaches for domain adaptation in ...

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Speaker and language Recognition Workshop, Odyssey 2014

作者： Shum, Stephen H. Reynolds, Douglas A. Garcia-Romero, Daniel McCree, Alan MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States MIT Lincoln Laboratory LexingtonMA United States JHU Human Language Technology Center of Excellence BaltimoreMD United States

In this paper, we motivate and define the domain adaptation challenge task for speaker recognition. Using an i-vector system trained only on out-of-domain data as a starting point, we propose a framework that utilizes large-scale clustering algorithms and unlabeled in-domain data to adapt the system for evaluation. In presenting the results and analyses of an empirical exploration of this problem, our initial findings suggest that, while perfect clustering yields the best results, imperfect clustering can still provide recognition performance within 15% of the optimal. We further present a system that achieves recognition performance comparable to one that is provided all knowledge of the domain mismatch, and lastly, we outline throughout this paper some of the many directions for future work that this new task provides. © Odyssey 2014: Speaker and language Recognition Workshop. All rights Reserved.

关键词： Clustering algorithms

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The NIST 2014 speaker recognition i-vector machine learning challenge

The NIST 2014 speaker recognition i-vector machine learning ...

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Speaker and language Recognition Workshop, Odyssey 2014

作者： Greenberg, Craig S. Bansé, Désiré Doddington, George R. Garcia-Romero, Daniel Godfrey, John J. Kinnunen, Tomi Martin, Alvin F. McCree, Alan Przybocki, Mark Reynolds, Douglas A. National Institute of Standards and Technology GaithersburgMD United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States University of Eastern Finland Finland MIT Lincoln Laboratory LexingtonMA United States

During late-2013 through mid-2014 NIST coordinated a special machine learning challenge based on the i-vector paradigm widely used by state-of-the-art speaker recognition systems. The i-vector challenge was run entirely online and used as source data fixed-length feature vectors projected into a low-dimensional space (i-vectors) rather than audio recordings. These changes made the challenge more readily accessible, enabled system comparison with consistency in the front-end and in the amount and type of training data, and facilitated exploration of many more approaches than would be possible in a single evaluation as traditionally run by NIST. Compared to the 2012 NIST Speaker Recognition Evaluation, the i-vector challenge saw approximately twice as many participants, and a nearly two orders of magnitude increase in the number of systems submitted for evaluation. Initial results indicate that the leading system achieved a relative improvement of approximately 38% over the baseline system. © Odyssey 2014: Speaker and language Recognition Workshop. All rights Reserved.

关键词： Machine learning

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Using social media to perform local influenza surveillance in an inner-city hospital: A retrospective observational study

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JMIR Public Health and Surveillance 2015年第1期1卷 e5页

作者： Broniatowski, David Andre Dredze, Mark Paul, Michael J. Dugas, Andrea Department of Engineering Management and Systems Engineering The George Washington University Washington DC United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States Department of Computer Science Johns Hopkins University Baltimore MD United States Department of Emergency Medicine Johns Hopkins University Baltimore MD United States

Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source ind

关键词： Social computing Time series analysis Web mining

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Frequency offset correction in speech without detecting pitch

Frequency offset correction in speech without detecting pitc...

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2013 38th IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2013

作者： Clark, Pascal Mallidi, Sri Harish Jansen, Aren Hermansky, Hynek Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University Baltimore MD United States

ISBN: (纸本)9781479903566

Radio-transmitted speech sometimes contains a residual frequency shift or offset, resulting from incorrect demodulation in single-sideband channels. Frequency-shifted speech can mask speaker identity and reduce intelligibility. Therefore, frequency offset will degrade the performance of downstream speech technologies. Existing offset correction methods require a pitch estimate of the speech signal, which is difficult in noisy radio channels. We present a new, automatic algorithm for detecting and correcting frequency offset, based on third-order modulation spectral analysis. Our method is remarkably simple and does not require pitch estimation. We provide derivations, examples, and a pilot study demonstrating how offset correction improves speaker verification for radio-transmitted speech. © 2013 IEEE.

关键词： Spectrum analysis

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