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Patrick Kenny - One of the best experts on this subject based on the ideXlab platform.
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modeling prosodic features with joint Factor Analysis for speaker verification
IEEE Transactions on Audio Speech and Language Processing, 2007Co-Authors: Najim Dehak, Pierre Dumouchel, Patrick KennyAbstract:In this paper, we introduce the use of continuous prosodic features for speaker recognition, and we show how they can be modeled using joint Factor Analysis. Similar features have been successfully used in language identification. These prosodic features are pitch and energy contours spanning a syllable-like unit. They are extracted using a basis consisting of Legendre polynomials. Since the feature vectors are continuous (rather than discrete), they can be modeled using a standard Gaussian mixture model (GMM). Furthermore, speaker and session variability effects can be modeled in the same way as in conventional joint Factor Analysis. We find that the best results are obtained when we use the information about the pitch, energy, and the duration of the unit all together. Testing on the core condition of NIST 2006 speaker recognition evaluation data gives an equal error rate of 16.6% and 14.6%, with prosodic features alone, for all trials and English-only trials, respectively. When the prosodic system is fused with a state-of-the-art cepstral joint Factor Analysis system, we obtain a relative improvement of 8% (all trials) and 12% (English only) compared to the cepstral system alone.
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Joint Factor Analysis versus eigenchannels in speaker recognition
IEEE Transactions on Audio Speech and Language Processing, 2007Co-Authors: Patrick Kenny, Pierre Ouellet, Gilles Boulianne, Pierre DumouchelAbstract:We compare two approaches to the problem of session variability in Gaussian mixture model (GMM)-based speaker verification, eigenchannels, and joint Factor Analysis, on the National Institute of Standards and Technology (NIST) 2005 speaker recognition evaluation data. We show how the two approaches can be implemented using essentially the same software at all stages except for the enrollment of target speakers. We demonstrate the effectiveness of zt-norm score normalization and a new decision criterion for speaker recognition which can handle large numbers of t-norm speakers and large numbers of speaker Factors at little computational cost. We found that Factor Analysis was far more effective than eigenchannel modeling. The best result we obtained was a detection cost of 0.016 on the core condition (all trials) of the evaluation
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joint Factor Analysis of speaker and session variability theory and algorithms
2006Co-Authors: Patrick KennyAbstract:We give a full account of the algorithms needed to carry out a joint Factor Analysis of speaker and session variability in a training set in which each speaker is recorded over many different channels and we discuss the practical limitations that will be encountered if these algorithms are implemented on very large data sets. This article is intended as a companion to (1) where we presented a new type of likelihood ratio statistic for speaker verification which is designed principally to deal with the problem of inter-session variability, that is the variability among recordings of a given speaker. This likelihood ratio statistic is based on a joint Factor Analysis of speaker and session variability in a training set in which each speaker is recorded over many different channels (such as one of the Switchboard II databases). Our purpose in the current article is to give detailed algorithms for carrying out such a Factor Analysis. Although we have only experimented with the applications of this model in speaker recognition we will also explain how it could serve as an integrated framework for progressive speaker-adaptation and on-line channel adaptation of HMM-based speech recognizers operating in situations where speaker identities are known. II. OVERVIEW OF THE JOINT Factor Analysis MODEL The joint Factor Analysis model can be viewed Gaussian distribution on speaker- and channel-dependent (or, more accurately, session-dependent) HMM supervectors in which most (but not all) of the variance in the supervector population is assumed to be accounted for by a small number of hidden variables which we refer to as speaker and channel Factors. The speaker Factors and the channel Factors play different roles in that, for a given speaker, the values of the speaker Factors are assumed to be the same for all recordings of the speaker but the channel Factors are assumed to vary from one recording to another. For example, the Gaussian distribution on speaker-dependent supervectors used in eigenvoice MAP (2) is a special case of the Factor Analysis model in which there are no channel Factors and all of the variance in the speaker- dependent HMM supervectors is assumed to be accounted The authors are with the Centre de recherche informatique de Montr´
Pierre Dumouchel - One of the best experts on this subject based on the ideXlab platform.
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Front-end Factor Analysis for speaker verification
IEEE Transactions on Audio Speech and Language Processing, 2011Co-Authors: Najim Dehak, Patrick J. Kenny, Reda Dehak, Pierre Dumouchel, Pierre OuelletAbstract:This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple Factor Analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate Analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint Factor Analysis scoring.
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modeling prosodic features with joint Factor Analysis for speaker verification
IEEE Transactions on Audio Speech and Language Processing, 2007Co-Authors: Najim Dehak, Pierre Dumouchel, Patrick KennyAbstract:In this paper, we introduce the use of continuous prosodic features for speaker recognition, and we show how they can be modeled using joint Factor Analysis. Similar features have been successfully used in language identification. These prosodic features are pitch and energy contours spanning a syllable-like unit. They are extracted using a basis consisting of Legendre polynomials. Since the feature vectors are continuous (rather than discrete), they can be modeled using a standard Gaussian mixture model (GMM). Furthermore, speaker and session variability effects can be modeled in the same way as in conventional joint Factor Analysis. We find that the best results are obtained when we use the information about the pitch, energy, and the duration of the unit all together. Testing on the core condition of NIST 2006 speaker recognition evaluation data gives an equal error rate of 16.6% and 14.6%, with prosodic features alone, for all trials and English-only trials, respectively. When the prosodic system is fused with a state-of-the-art cepstral joint Factor Analysis system, we obtain a relative improvement of 8% (all trials) and 12% (English only) compared to the cepstral system alone.
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Joint Factor Analysis versus eigenchannels in speaker recognition
IEEE Transactions on Audio Speech and Language Processing, 2007Co-Authors: Patrick Kenny, Pierre Ouellet, Gilles Boulianne, Pierre DumouchelAbstract:We compare two approaches to the problem of session variability in Gaussian mixture model (GMM)-based speaker verification, eigenchannels, and joint Factor Analysis, on the National Institute of Standards and Technology (NIST) 2005 speaker recognition evaluation data. We show how the two approaches can be implemented using essentially the same software at all stages except for the enrollment of target speakers. We demonstrate the effectiveness of zt-norm score normalization and a new decision criterion for speaker recognition which can handle large numbers of t-norm speakers and large numbers of speaker Factors at little computational cost. We found that Factor Analysis was far more effective than eigenchannel modeling. The best result we obtained was a detection cost of 0.016 on the core condition (all trials) of the evaluation
Brian D. Haig - One of the best experts on this subject based on the ideXlab platform.
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exploratory Factor Analysis theory generation and scientific method
Multivariate Behavioral Research, 2005Co-Authors: Brian D. HaigAbstract:This article examines the methodological foundations of exploratory Factor Analysis (EFA) and suggests that it is properly construed as a method for generating explanatory theories. In the first half of the article it is argued that EFA should be understood as an abductive method of theory generation that exploits an important precept of scientific inference known as the principle of the common cause. This characterization of the inferential nature of EFA coheres well with its interpretation as a latent variable method. The second half of the article outlines a broad theory of scientific method in which abductive reasoning figures prominently. It then discusses a number of methodological features of EFA in the light of that method. Specifically, it is argued that EFA helps researchers generate theories with genuine explanatory merit; that Factor indeterminacy is a methodological challenge for both EFA and confirmatory Factor Analysis, but that the challenge can be satisFactorily met in each case; and, tha...
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exploratory Factor Analysis theory generation and scientific method
Multivariate Behavioral Research, 2005Co-Authors: Brian D. HaigAbstract:This article examines the methodological foundations of exploratory Factor Analysis (EFA) and suggests that it is properly construed as a method for generating explanatory theories. In the first half of the article it is argued that EFA should be understood as an abductive method of theory generation that exploits an important precept of scientific inference known as the principle of the common cause. This characterization of the inferential nature of EFA coheres well with its interpretation as a latent variable method. The second half of the article outlines a broad theory of scientific method in which abductive reasoning figures prominently. It then discusses a number of methodological features of EFA in the light of that method. Specifically, it is argued that EFA helps researchers generate theories with genuine explanatory merit; that Factor indeterminacy is a methodological challenge for both EFA and confirmatory Factor Analysis, but that the challenge can be satisFactorily met in each case; and, that EFA, as a useful method of theory generation, can be profitably employed in tandem with confirmatory Factor Analysis and other methods of theory evaluation.
Bruce Thompson - One of the best experts on this subject based on the ideXlab platform.
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applying the bootstrap to the multivariate case bootstrap component Factor Analysis
Behavior Research Methods, 2007Co-Authors: Linda Reichwein Zientek, Bruce ThompsonAbstract:The bootstrap method, which empirically estimates the sampling distribution for either inferential or descriptive sstatistical purposes, can be applied to the multivariate case. When conducting bootstrap component, or Factor, Analysis, resampling results must be located in a common Factor space before summary statistics for each estimated parameter can be computed. The present article describes a strategy for applying the bootstrap method to conduct either a bootstrap component or a Factor Analysis with a program syntax for SPSS. The Holzinger–Swineford data set is employed to make the discussion more concrete.
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exploratory and confirmatory Factor Analysis understanding concepts and applications
2004Co-Authors: Bruce ThompsonAbstract:This volume presents the important concepts required for implementing two disciplines of Factor Analysis - exploratory Factor Analysis (EFA) and confirmatory Factor Analysis (CFA) with an emphasis on EFA/CFA linkages. Modern extensions of older data Analysis methods (e.g., ANOVA, regression, MANOVA, and descriptive discriminant Analysis) have brought theory-testing procedures to the analytic forefront. Variations of Factor Analysis, such as the Factoring of people or time, have great potential to inform psychological research. Thompson deftly presents highly technical material in an appealing and accessible manner. The book is unique in that it presents both exploratory and confirmatory methods within the single category of the general linear model (GLM). Canons of best Factor analytic practice are presented and explained. An actual data set, generated by 100 graduate students and 100 faculty from the United States and Canada, is used throughout the book, allowing readers to replicate reported results.
Bjt Morgan - One of the best experts on this subject based on the ideXlab platform.
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principal component Analysis and exploratory Factor Analysis
Statistical Methods in Medical Research, 1992Co-Authors: I T Joliffe, Bjt MorganAbstract:In this paper we compare and contrast the objectives of principal component Analysis and exploratory Factor Analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to Factor Analysis, it is pointed out that in some cases it may be useful to rotate certain principal components if and when that is appropriate.
