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Syllable category based short utterance speaker recognition

Syllable category based short utterance speaker recognition

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International Conference on Audio, language and Image Processing, ICALIP

作者： Nakhat Fatima Thomas Fang Zheng Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

In Short Utterance Speaker Recognition (SUSR), the role of complete speech units like syllables in carrying speaker information needs further investigation. This paper presents a novel method of using syllable categories for SUSR. We define Syllable Categories (SCs) with the help of syllable structure of Chinese language. Syllables in speech are segmented into SCs, which are then used to develop Universal Background SC Model for each SC. Conventional GMM-UBM system is used for training and testing. The proposed categories give average EER of 17.79%, 19.35% and 21.65% for 3, 2 and 1 second of test utterance length respectively. Experimental results show that in text dependent SUSR, significant speaker-specific information is present at syllable level where prosodic idiosyncrasies can be utilized. This information can be used in SUSR by exploiting similarities in consonants and vowels of a syllable such that SCs can be used effectively.

关键词： Speaker recognition Speech Speech recognition Training Feature extraction Hidden Markov models Liquids

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Text-Dependent Speaker Recognition with long-term features based on functional data analysis

Text-Dependent Speaker Recognition with long-term features b...

引用

International Symposium on Chinese Spoken language Processing

作者： Chenhao Zhang Thomas Fang Zheng Ruxin Chen Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Research and Development Sony Computer Entertainment America Foster City CA USA

Text-Dependent Speaker Recognition (TDSR) is widely used nowadays. The short-term features like Mel-Frequency Cepstral Coefficient (MFCC) have been the dominant features used in traditional Dynamic Time Warping (DTW) based TDSR systems. The short-term features capture better local portion of the significant temporal dynamics but worse in overall sentence statistical characteristics. Functional Data Analysis (FDA) has been proven to show significant advantage in exploring the statistic information of data, so in this paper, a long-term feature extraction based on MFCC and FDA theory is proposed, where the extraction procedure consists of the following steps: Firstly, the FDA theory is applied after the MFCC feature extraction; Secondly, for the purpose of compressing the redundant data information, new feature based on the Functional Principle Component Analysis (FPCA) is generated; Thirdly, the distance between train features and test features is calculated for the use of the recognition procedure. Compared with the existing MFCC plus DTW method, experimental results show that the new features extracted with the proposed method plus the cosine similarity measure demonstrates better performance.

关键词： Mel frequency cepstral coefficient Feature extraction Speech Speech recognition Speaker recognition Data analysis Fitting

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Name Phylogeny: A Generative Model of String Variation 12

Name Phylogeny: A Generative Model of String Variation

引用

Conference on Empirical Methods in Natural language Processing

作者： Nicholas Andrews Jason Eisner Mark Dredze Department of Computer Science and Human Language Technology Center of Excellence Johns Hopkins University 3400 N. Charles St. Baltimore MD 21218 USA

ISBN: (纸本)9781622765034

Many linguistic and textual processes involve transduc-tion of strings. We show how to learn a stochastic transducer from an unorganized collection of strings (rather than string pairs). The role of the transducer is to organize the collection. Our generative model explains similarities among the strings by supposing that some strings in the collection were not generated ab initio, but were instead derived by transduction from other, "similar" strings in the collection. Our variational EM learning algorithm alternately reestimates this phylogeny and the transducer parameters. The final learned transducer can quickly link any test name into the final phylogeny, thereby locating variants of the test name. We find that our method can effectively find name variants in a corpus of web strings used to refer to persons in Wikipedia, improving over standard untrained distances such as Jaro-Winkler and Leven-shtein distance.

关键词： Cosmic strings Transducers Phylogeny collection Transduction

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Towards a Predicate-Argument Evaluation for MT 12

Towards a Predicate-Argument Evaluation for MT

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Workshop on Syntax and Structure in Statistical Translation

作者： Ondrej Bojar Dekai Wu Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781627483452

HMEANT (Lo and Wu, 2011a) is a manual MT evaluation technique that focuses on predicate-argument structure of the sentence. We relate HMEANT to an established linguistic theory, highlighting the possibilities of reusing existing knowledge and resources for interpreting and automating HMEANT. We apply HMEANT to a new language, Czech in particular, by evaluating a set of English-to-Czech MT systems. HMEANT proves to correlate with manual rankings at the sentence . level better than a range of automatic metrics. However, the main contribution of this paper is the identification of several issues of HMEANT annotation and our proposal on how to resolve them.

关键词： machine translation MT Linguistic Theory Probability manual evaluation method Adjudication Automation

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A method for analysis and correction of cross-talk effects in pathway analysis

A method for analysis and correction of cross-talk effects i...

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International Joint Conference on Neural Networks

作者： Michele Donato Sorin Draghici Alin Tomoiaga Peter Westfall Zhonghui Xu Roberto Romero Department of Computer Science Wayne State University Center for Advanced Analytics and Business Intelligence Texas Tech University Perinatology Research Branch National Institute of Child Health and Human Development National Institutes of Health Department of Health and Human Services

ISBN: (纸本)9781467314886

Many analysis techniques are currently available to identify the signaling pathways significantly impacted in a given condition. All these approaches calculate a p-value that aims to quantify the significance of the involvement of a given pathway in the condition under study. These p-values were thought to be related to the likelihood of their respective pathways being involved in the given condition, and to be independent. Here we show that this is not true, and that many pathways are not independent and that can considerably affect each other's p-values through a phenomenon we refer to as "cross-talk." Thus, the significance of a given pathway in a given experiment has to be interpreted in the context of the other pathways that appear to be significant. Using real data, we show that in same cases pathways with significant classical p-values are not biologically meaningful, and that some biologically meaningful pathways with insignificant p-values become significant when the crosstalk effects of other pathways are removed. We show that this phenomenon is related to the amount of common genes between different pathways, affecting the most widely used methods for pathway analysis, and we propose an analysis technique that is able to correct the over-enrichment significance of a pathway when the cross-talk effects of other pathways are removed.

关键词： Couplings Indexes Parkinson's disease Muscles Vectors

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New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure

New ℌ∞ bounds for the recursive least squares algorithm ex...

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

作者： Koby Crammer Alex Kulesza Mark Dredze Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

The recursive least squares (RLS) algorithm is well known and has been widely used for many years. Most analyses of RLS have assumed statistical properties of the data or the noise process, but recent robust ℌ ∞ analyses have been used to bound the ratio of the performance of the algorithm to the total noise. In this paper, we provide an additive analysis bounding the difference between performance and noise. Our analysis provides additional convergence guarantees in general, and particular benefits for structured input data. We illustrate the analysis using human speech and white noise.

关键词： Noise Additives Signal processing algorithms Algorithm design and analysis Speech Learning systems Prediction algorithms

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Malpractice and malcontent: Analyzing medical complaints in twitter

Malpractice and malcontent: Analyzing medical complaints in ...

引用

2012 AAAI Fall Symposium

作者： Nakhasi, Atul Passarella, Ralph J. Bell, Sarah G. Paul, Michael J. Dredze, Mark Pronovost, PeterJ Johns Hopkins University School of Medicine Johns Hopkins University Baltimore MD United States Departments of Anesthesiology/Critical Care Medicine and Surgery Johns Hopkins University Baltimore MD United States Department of Computer Science and Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States University of Michigan Medical School Ann Arbor MI United States

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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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A Voxel Selection Method for the Multivariate Analysis of Imaging Genetics Data

A Voxel Selection Method for the Multivariate Analysis of Im...

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International Joint Conference on Neural Networks

作者： Sambu Seo Johannes Mohr Hauke Heekeren Andreas Heinz Ben Eppinger Shu-Chen Li Klaus Obermayer School of Electrical Engineering and Computer Science Berlin Institute of Technology Department of Education and Psychology Freie Universitat Department of Psychiatry and Psychotherapy (CCM) Charite University Medical Center Center for Lifespan Psychology Max Planck Institute for Human Development

ISBN: (纸本)9781467314886

We suggest a multivariate genotype-phenotype association test for functional magnetic resonance imaging (fMRI) data. The method uses a voxel selection and ranking scheme based on iterative adaptive Lasso for defining a functional region of interest. A classifier-based test is used to assess the significance of potential associations between the differential activity pattern within that region and a set of candidate genetic variants. We applied the method to a small sample dataset from an ongoing imaging genetics study and identified a significant genetic association to a stimulus-locked imaging phenotype.

关键词： Genetics Imaging Testing Training Prediction algorithms Algorithm design and analysis Markov processes

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The RWTH 2010 Quaero ASR evaluation system for English, French, and German

The RWTH 2010 Quaero ASR evaluation system for English, Fren...

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36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011

作者： Sundermeyer, M. Nussbaum-Thom, M. Wiesler, S. Plahl, C. El-Desoky Mousa, A. Hahn, S. Nolden, D. Schlüter, R. Ney, H. Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781457705397

关键词： Speech recognition

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