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Jet mass and substructure of inclusive jets in √s = 7TeV pp collisions with the ATLAS experiment

  • The ATLAS collaboration
  • Aad, G.
  • Arnal, V.
  • Barreiro, F.
  • Cantero, J.
  • de la Torre, H.
  • del Peso, J.
  • Glasman, C.
  • Labarga, L.
  • Lagouri, T.
  • Llorente Merino, J.
  • March, L.
  • Nebot, E.
Journal of High Energy Physics 2012.5 (2012): 128 reproduced by permission of Scuola Internazionale Superiore di Studi Avanzati (SISSA), Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAM, Recent studies have highlighted the potential of jet substructure techniques to identify the hadronic decays of boosted heavy particles. These studies all rely upon the assumption that the internal substructure of jets generated by QCD radiation is well understood. In this article, this assumption is tested on an inclusive sample of jets recorded with the ATLAS detector in 2010, which corresponds to 35 pb -1 of pp collisions delivered by the LHC at Rs = 7TeV. In a subsample of events with single pp collisions, measurements corrected for detector efficiency and resolution are presented with full systematic uncertainties. Jet invariant mass, kt splitting scales and N-subjettiness variables are presented for anti-kt R = 1.0 jets and Cambridge-Aachen R = 1.2 jets. Jet invariant-mass spectra for Cambridge-Aachen R = 1.2 jets after a splitting and filtering procedure are also presented. Leading-order parton-shower Monte Carlo predictions for these variables are found to be broadly in agreement with data. The dependence of mean jet mass on additional pp interactions is also explored
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Analyzing training dependencies and posterior fusion in discriminant classification of apnea patients based on sustained and connected speech

  • Blanco, José Luis
  • Fernández Pozo, Rubén
  • Toledano, Doroteo T.
  • Caminero, F. Javier
  • López Gonzalo, Eduardo
Proceedings of Interspeech 2011, Florence (Italy), We present a novel approach using both sustained vowels and connected speech, to detect obstructive sleep apnea (OSA) cases within a homogeneous group of speakers. The proposed scheme is based on state-of-the-art GMM-based classifiers, and acknowledges specifically the way in which acoustic models are trained on standard databases, as well as the complexity of the resulting models and their adaptation to specific data. Our experimental database contains a suitable number of utterances and sustained speech from healthy (i.e control) and OSA Spanish speakers. Finally, a 25.1% relative reduction in classification error is achieved when fusing continuous and sustained speech classifiers., The activities described in this paper were funded by the Spanish Ministry of Science and Innovation as part of the TEC2009-14719-C02-02 (PriorSpeech) project.
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Forensic speaker recognition using traditional features comparing automatic and human-in-the-loop formant tracking

  • Castro, Alberto de
  • Ramos, Daniel
  • González-Rodríguez, Joaquín
Proceedings of Interspeech 2009, Brighton (United Kingdom), In this paper we compare forensic speaker recognition with traditional features using two different formant tracking strategies: one performed automatically and one semi-automatic performed by human experts. The main contribution of the work is the use of an automatic method for formant tracking, which allows a much faster recognition process and the use of a much higher amount of data for modelling background population, calibration, etc. This is especially important in likelihood-ratio-based forensic speaker recognition, where the variation of features among a population of speakers must be modelled in a statistically robust way. Experiments show that, although recognition using the human-in-the-loop approach is better than using the automatic scheme, the performance of the latter is also acceptable. Moreover, we present a novel feature selection method which allows the analysis of which feature of each formant has a greater contribution to the discriminating power of the whole recognition process, which can be used by the expert in order to decide which features in the available speech material are important., This work has been funded by the Spanish Ministry of Education under project TEC2006-13170-C02-01.
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Speaker recognition using temporal contours in linguistic units: the case of formant and formant-bandwidth trajectories

  • González-Rodríguez, Joaquín
Proceedings of Interspeech 2011, Florence (Italy), We describe a new approach to automatic speaker recognition based in explicit modeling of temporal contours in linguistic units (TCLU). Inspired in successful work in forensic speaker identification, we extend the approach to design a fully automatic system, with a high potential for combination with spectral systems. Using SRI's Decipher phone, word and syllabic labels, we have tested up to 468 unit-based subsystems from 6 groups of lexically-determined units, namely phones, diphones, triphones, center phone in triphones, syllables and words, subsystems being combined at the score level. Evaluating with NIST SRE04 English-only 1s1s, their hierarchical fusion gives an EER of 4.20% (minDCF=0.018) from automatic formant tracking of conversational telephone speech. Combining extremely well with a Joint Factor Analysis system (from JFA EER of 4.25% to 2.47%, minDCF from 0.020 to 0.012), extensions as more robust prosodic or spectral features are likely to further improve this approach., This work has been supported by MEC research stay grant PR-2010-123, MICINN project TEC09-14179, ForBayes project CCG10-UAM/TIC-5792 and Catedra UAM-Telefonica.
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Speaker dependent emotion recognition using prosodic supervectors

  • López Moreno, Ignacio
  • Ortego Resa, Carlos
  • González-Rodríguez, Joaquín
  • Ramos, Daniel
Proceedings of Interspeech 2009, Brighton (United Kingdom), This work presents a novel approach for detection of emotions embedded in the speech signal. The proposed approach works at the prosodic level, and models the statistical distribution of the prosodic features with Gaussian Mixture Models (GMM) meanadapted from a Universal Background Model (UBM). This allows the use of GMM-mean supervectors, which are classified by a Support Vector Machine (SVM). Our proposal is compared to a popular baseline, which classifies with an SVM a set of selected prosodic features from the whole speech signal. In order to measure the speaker intervariability, which is a factor of degradation in this task, speaker dependent and speaker independent frameworks have been considered. Experiments have been carried out under the SUSAS subcorpus, including real and simulated emotions. Results shows that in a speaker dependent framework our proposed approach achieves a relative improvement greater than 14% in Equal Error Rate (EER) with respect to the baseline approach. The relative improvement is greater than 17% when both approaches are combined together by fusion with respect to the baseline.
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Addressing database mismatch in forensic speaker recognition with Ahumada III: A public real-casework database in Spanish

  • Ramos, Daniel
  • González-Rodríguez, Joaquín
  • González Domínguez, Javier
  • Lucena-Molina, Jose Juan
Proceedings of Interspeech 2008, Brisbane (Australia), This paper presents and describes Ahumada III, a speech database in Spanish collected from real forensic cases. In its current release, the database presents 61 male speakers recorded using the systems and procedures followed by Spanish Guardia Civil police force. The paper also explores the usefulness of such a corpus for facing the important problem of database mismatch in speaker recognition, understood as the difference between the database used for tuning a speaker recognition system and the data which the system will handle in operational conditions. This problem is typical in forensics, where variability in speech conditions may be extreme and difficult to model. Therefore, this work also presents a study evaluating the impact of such problem, for which a corpus quoted as NIST4M (NIST MultiMic MisMatch) has been constructed from NIST SRE 2006 data. NIST4M presents microphone data both in the enrolled models and in the test segments, allowing the generation of trials in a variety of strongly mismatching conditions. Database mismatch is simulated by eliminating some microphone channels of interest from the background data, and computing scores with speech from such microphones in unknown testing conditions as usually happens in forensic speaker recognition. Finally, we show how the incorporation of Ahumada III as background data is useful to face database mismatch in real-world forensic conditions., This work has been supported by the Spanish Ministry of Education under project TEC2006-13170-C02-01.
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Anchor-Model Fusion for Language Recognition

  • López Moreno, Ignacio
  • Ramos, Daniel
  • González-Rodríguez, Joaquín
  • Toledano, Doroteo T.
Proceedings of Interspeech 2008, Brisbane (Australia), State-of-the-art language recognition systems usually combine multiple acoustic and phonotactic subsystems. The outputs of those systems are usually fused in different ways but the score from a trial is always obtained from N scores from N subsystems. In this paper, a robust novel approach to subsystem fusion in language recognition is proposed based on the relative performance of each trial not just to the claimed model but to all available models. The proposed technique exploits the relative behavior of a given speech utterance over the cohort of anchor models from the different subsystems, resulting in the proposed anchor-model fusion. Experiments fusing seven phone-SVM subsystems submitted by the authors to NIST LRE 2007 assess the robustness to non-uniform data availability over rule-based and trained fusion schemes as linear kernel SVM, as well as significant improvements in performance both in average EER and Cavg as used in NIST LRE., This work was funded by the Spanish Ministry of Science and Technology under project TEC2006-13170-C02-01.
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Forensic automatic speaker recognition: fiction or science?

  • González-Rodríguez, Joaquín
Proceedings of Interspeech 2008, Brisbane (Australia), Incluye presentación en Power Point ofrecida durante el congreso., Hollywood films and CSI-like movies show a technology landscape far from real, both in forensic speaker recognition and other identification-of-the-source forensic areas. Lay persons are used to good-looking scientist-and-investigators performing voice identifications ("we got a match!") or smart fancy devices producing voice transformations causing one actor to instantaneously talk with the voice of other. Simultaneously, Forensic Identification Science is facing a global challenge impelled firstly by progressively higher requirements for admissibility of expert testimony in Court and secondly by the transparent and testable nature of DNA typing, which is now seen as the new gold-standard model of a scientifically defensible approach to be emulated by all other identification-of-the-source areas. In this presentation we will show how forensic speaker recognition can comply with the requirements of transparency and testability in forensic science This will lead to fulfilling the court requirements about role separation between scientists and judges/juries, and bring about integration in a forensically adequate framework in which the scientist provides the appropriate information necessary to the court's decision processes.
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MAP and sub-word level T-norm for text-dependent speaker recognition

  • Toledano, Doroteo T.
  • Hernández López, Daniel
  • Esteve Elizalde, Cristina
  • González-Rodríguez, Joaquín
  • Fernández Pozo, Rubén
  • Hernández Gómez, Luis
Proceedings of Interspeech 2008, Brisbane (Australia), This paper presents improvements in text-dependent speaker recognition based on the use of Maximum A Posteriori (MAP) adaptation of Hidden Markov Models and the use of new sub-word level T-Normalization procedures. Results on the YOHO corpus show that the use of MAP adaptation provides a relative improvement of 22.6% in Equal Error Rate (EER) in comparison with Baum-Welch retraining and Maximum Likelihood Linear Regression (MLLR) adaptation. The newly proposed sub-word level T-Normalization procedures provide additional relative improvements, particularly for small cohorts, of up to 20% in EER in comparison with the normal utterance-level T-Normalization., This work was funded by the Spanish Ministry of Science and Technology under project TEC2006-13170-C02-01.
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Understanding e-learning adoption in Brazil: major determinants and gender effects

  • Okazaki, Shintaro
  • Renda dos Santos, Luiz Miguel
The objective of this study is to examine factors influencing e-learning adoption and the moderating role of gender. This study extends the technology acceptance model (TAM) by adding attitude and social interaction. The new construct of social interaction is applied to the South American context. Gender effects on e-learning adoption from educators’ perspectives have seldom been explored. The data collection takes place in three major Brazilian universities. In total, 446 faculty members responded to the questionnaire. Our structural equation modeling reveals that ease of use and perceived usefulness are significant antecedents of attitude, which in turn affects intention. However, unlike the original TAM, perceived usefulness is not a direct driver of intention. In terms of moderation, gender affects three relationships: (1) ease of use –› perceived usefulness; (2) perceived usefulness –› attitude, and (3) intention –› actual behavior. The analysis is carried out in a single country; thus, caution should be taken in generalization of the results. The findings will help academics, educators, and policy makers to better understand the mechanism of e-learning adoption in Brazil.
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