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COMPLEMENTARY ENVELOPE ESTIMATION FOR FREQUENCY-MODULATED RANDOM SIGNALS

COMPLEMENTARY ENVELOPE ESTIMATION FOR FREQUENCY-MODULATED RA...

引用

IEEE International Conference on Acoustics, speech and Signal processing

作者： Pascal Clark Ivars Kirsteins Les Atlas Johns Hopkins University Human Language Technology Center of Excellence Naval Undersea Warfare Center Newport Department of Electrical Engineering University of Washington

ISBN: (纸本)9781479903573

The Hilbert envelope is a well-known indicator for amplitude modulation in subband time series. However, frequency modulation estimators typically impose temporal smoothness constraints that limit their general use for stochastic signals. We introduce the complementary envelope as a direct indicator of stochastic phase coherence and frequency modulation. Rooted in the second-order statistics of randomly-phased sinusoids, the complementary envelope is distinct from the Hilbert envelope, and can be estimated easily and separately. We propose a new complementary envelope estimator based on modified multitaper spectral analysis, and use it to reveal previously unseen FM-like behavior in ship propeller noise.

关键词： Modulation Complementary statistics Impropriety Multitaper Time-frequency

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SUPERVECTOR BAYESIAN SPEAKER COMPARISON

SUPERVECTOR BAYESIAN SPEAKER COMPARISON

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IEEE International Conference on Acoustics, speech and Signal processing

作者： Bengt J. Borgstrom Alan McCree MIT Lincoln Laboratory Lexington MA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD

ISBN: (纸本)9781479903573

In this paper we propose fully Bayesian speaker comparison of supervectors, which we refer to as SV-BSC, as a method for estimating whether a test cut was generated by the same speaker as an enrollment set. We derive the SV-BSC log-likelihood ratio of same-speaker to different-speaker hypotheses, and present solutions for model training and Bayesian scoring. We then show that if speaker and channel variability are assumed to inhabit a total variability sub-space, SV-BSC scoring reduces to a form which requires only low-computation subspace operations. Finally, we show that common speaker recognition techniques such as Joint Factor Analysis (JFA) and i-vector Probabilistic Linear Discriminant Analysis (PLDA) are approximations to this full solution under certain additional assumptions. Experiments on the NIST 2010 SRE show SV-BSC to outperform a PLDA system.

关键词： Loudspeakers test cutting speaker recognition Subspace joint factor Model trains Scoring Bayesian inference

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Joshua 4.0: Packing, PRO, and Paraphrases 7

Joshua 4.0: Packing, PRO, and Paraphrases

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7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Ganitkevitch, Juri Cao, Yuan Weese, Jonathan Post, Matt Callison-Burch, Chris Center For Language And Speech Processing Human Language Technology Center Of Excellence Johns Hopkins University United States

ISBN: (纸本)9781937284206

We present Joshua 4.0, the newest version of our open-source decoder for parsing-based statistical machine translation. The main contributions in this release are the introduction of a compact grammar representation based on packed tries, and the integration of our implementation of pairwise ranking optimization, J-PRO. We further present the extension of the Thrax SCFG grammar extractor to pivot-based extraction of syntactically informed sentential paraphrases. ©2012 Association for Computational Linguistics.

关键词： Machine translation

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Shared components topic models

Shared components topic models

引用

2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2012

作者： Gormley, Matthew R. Dredze, Mark Van Durme, Benjamin Eisner, Jason Center for Language and Speech Processing Human Language Technology Center of Excellence Department of Computer Science BaltimoreMD United States

ISBN: (纸本)1937284204

With a few exceptions, extensions to latent Dirichlet allocation (LDA) have focused on the distribution over topics for each document. Much less attention has been given to the underlying structure of the topics themselves. As a result, most topic models generate topics independently from a single underlying distribution and require millions of parameters, in the form of multinomial distributions over the vocabulary. In this paper, we introduce the Shared Components Topic Model (SCTM), in which each topic is a normalized product of a smaller number of underlying component distributions. Our model learns these component distributions and the structure of how to combine subsets of them into topics. The SCTM can represent topics in a much more compact representation than LDA and achieves better perplexity with fewer parameters. © 2012 Association for Computational Linguistics.

关键词： Statistics

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Revisiting the case for explicit syntactic information in language models

Revisiting the case for explicit syntactic information in la...

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2012 Workshop on Will We Ever Really Replace the N-gram Model? On the Future of language Modeling for HLT, WLM 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2012

作者： Rastrow, Ariya Khudanpur, Sanjeev Dredze, Mark Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University BaltimoreMD United States

Statistical language models used in deployed systems for speech recognition, machine translation and other human language technologies are almost exclusively n-gram models. They are regarded as linguistically naïve, but estimating them from any amount of text, large or small, is straightforward. Furthermore, they have doggedly matched or outperformed numerous competing proposals for syntactically well-motivated models. This unusual resilience of n-grams, as well as their weaknesses, are examined here. It is demonstrated that n-grams are good word-predictors, even linguistically speaking, in a large majority of word-positions, and it is suggested that to improve over n-grams, one must explore syntax-aware (or other) language models that focus on positions where n-grams are weak. © 2012 Association for Computational Linguistics.

关键词： speech recognition

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Discriminatively trained Bayesian speaker comparison of i-vectors

Discriminatively trained Bayesian speaker comparison of i-ve...

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

作者： Bengt J. Borgström Alan McCree Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479903573

This paper presents a framework for fully Bayesian speaker comparison of i-vectors. By generalizing the train/test paradigm, we derive an analytic expression for the speaker comparison log-likelihood ratio (LLR), as well as solutions for model training and Bayesian scoring. This framework is useful for enrollment sets of any size. For the specific case of single-cut enrollment, it is shown to be mathematically equivalent to probabilistic linear discriminant analysis (PLDA). Additionally, we present discriminative training of model hyper-parameters by minimizing the total cross entropy between LLRs and class labels. When applied to speaker recognition, significant performance gains are observed for various NIST SRE 2010 extended evaluation tasks.

关键词： Training Bayes methods Speaker recognition Covariance matrices Entropy NIST Linear programming

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Experimenting with drugs (and topic models): Multi-dimensional exploration of recreational drug discussions

Experimenting with drugs (and topic models): Multi-dimension...

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2012 AAAI Fall Symposium

作者： Paul, Michael J. Dredze, Mark Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21218 United States

Clinical research of new recreational drugs and trends requires mining current information from non-traditional text sources. In this work we support such research through the use of a multi-dimensional latent text model - factorial LDA - that captures orthogonal factors of corpora, creating structured output for researchers to better understand the contents of a corpus. Since a purely unsupervised model is unlikely to discover specific factors of interest to clinical researchers, we modify the structure of factorial LDA to incorporate prior knowledge, including the use of of observed variables, informative priors and background components. The resulting model learns factors that correspond to drug type, delivery method (smoking, injection, etc.), and aspect (chemistry, culture, effects, health, usage). We demonstrate that the improved model yields better quantitative and more interpretable results. Copyright © 2012, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Clinical research

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Dirt cheap web-scale parallel text from the Common Crawl

Dirt cheap web-scale parallel text from the Common Crawl

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Smith, Jason R. Koehn, Philipp Saint-Amand, Herve Callison-Burch, Chris Plamada, Magdalena Lopez, Adam Department of Computer Science Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States School of Informatics University of Edinburgh United Kingdom Institute of Computational Linguistics University of Zurich Switzerland Computer and Information Science Department University of Pennsylvania United States

ISBN: (纸本)9781937284503

Parallel text is the fuel that drives modern machine translation systems. The Web is a comprehensive source of preexisting parallel text, but crawling the entire web is impossible for all but the largest companies. We bring web-scale parallel text to the masses by mining the Common Crawl, a public Web crawl hosted on Amazon's Elastic Cloud. Starting from nothing more than a set of common two-letter language codes, our open-source extension of the STRAND algorithm mined 32 terabytes of the crawl in just under a day, at a cost of about $500. Our large-scale experiment uncovers large amounts of parallel text in dozens of language pairs across a variety of domains and genres, some previously unavailable in curated datasets. Even with minimal cleaning and filtering, the resulting data boosts translation performance across the board for five different language pairs in the news domain, and on open domain test sets we see improvements of up to 5 BLEU. We make our code and data available for other researchers seeking to mine this rich new data resource.1 © 2013 Association for Computational Linguistics.

关键词： Digital storage

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A Co-Robotic Assistant Capable of Object Selection and Search via a Brain Machine Interface

A Co-Robotic Assistant Capable of Object Selection and Searc...

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International IEEE/EMBS Conference on Neural Engineering

作者： Varsha Shankar Lena Sherbakov Byron Galbraith Aisha Sohail Gennady Livitz Anatoli Gorchetchnikov Heather Ames Frank H. Guenther Massimiliano Versace GRASP Laboratory University of Pennsylvania Boston University and CELEST Center of Excellence for Learning in Education Science and Technology Departments of Speech Language & Hearing Sciences and Biomedical Engineering Boston University

ISBN: (纸本)9781467319676

Co-robotic assistants, or Cobots can improve the quality of life for individuals with locked-in syndrome (LIS) by allowing augmented control over their surroundings. Implemented in collaboration between the Boston University Neuromorphics Lab and Neural Prosthesis Lab, this work provides a proof of concept of an autonomous robot coupled with a non-invasive brain machine interface (BMI). The system uses Steady State Visually Evoked Potential (SSVEP) for target selection and a massively parallel neural network that models functionality of the primate "where" and "what" visual path-ways. The simulated visual processes perform object recognition on images streamed from the Cobot equipped with a pan-and-tilt camera. In this paper, we describe a subcomponent of the system designed to allow the neural network to learn the identity of objects, the user to select a target object and the Cobot to perform autonomous visual investigation.

关键词： main machine interface human machine interaction machine Autonomous robots target selection Neural network Visualization parallel neural network object recognition Primates Cerebrum Locked-In Syndrome Neural Prostheses

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Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings

Fixed-dimensional acoustic embeddings of variable-length seg...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： Keith Levin Katharine Henry Aren Jansen Karen Livescu Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA University of Chicago Chicago IL USA Toyota Technological Institute at Chicago Chicago IL USA

ISBN: (纸本)9781479927579

Measures of acoustic similarity between words or other units are critical for segmental exemplar-based acoustic models, spoken term discovery, and query-by-example search. Dynamic time warping (DTW) alignment cost has been the most commonly used measure, but it has well-known inadequacies. Some recently proposed alternatives require large amounts of training data. In the interest of finding more efficient, accurate, and low-resource alternatives, we consider the problem of embedding speech segments of arbitrary length into fixed-dimensional spaces in which simple distances (such as cosine or Euclidean) serve as a proxy for linguistically meaningful (phonetic, lexical, etc.) dissimilarities. Such embeddings would enable efficient audio indexing and permit application of standard distance learning techniques to segmental acoustic modeling. In this paper, we explore several supervised and unsupervised approaches to this problem and evaluate them on an acoustic word discrimination task. We identify several embedding algorithms that match or improve upon the DTW baseline in low-resource settings.

关键词： Vectors Acoustics Laplace equations Principal component analysis Time series analysis speech Training

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