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Front End Factor Analysis for Speaker Verification

by Najim Dehak, Patrick J. Kenny, Réda Dehak, Pierre Dumouchel, Pierre Ouellet - IEEE Transactions on Audio, Speech and Language Processing , 2010
"... Abstract—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 ..."
Abstract - Cited by 315 (22 self) - Add to MetaCart
Abstract—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

Graphical models and automatic speech recognition

by Jeffrey A. Bilmes - Mathematical Foundations of Speech and Language Processing , 2003
"... Graphical models provide a promising paradigm to study both existing and novel techniques for automatic speech recognition. This paper first provides a brief overview of graphical models and their uses as statistical models. It is then shown that the statistical assumptions behind many pattern recog ..."
Abstract - Cited by 78 (15 self) - Add to MetaCart
recognition techniques commonly used as part of a speech recognition system can be described by a graph – this includes Gaussian distributions, mixture models, decision trees, factor analysis, principle component analysis, linear discriminant analysis, and hidden Markov models. Moreover, this paper shows

SVM based speaker verification using a GMM supervector kernel and NAP variability compensation

by W. M. Campbell, D. E. Sturim, D. A. Reynolds, A. Solomonoff - in Proceedings of ICASSP, 2006
"... Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent d ..."
Abstract - Cited by 161 (16 self) - Add to MetaCart
discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation. We consider this GMM supervector in the context of support vector machines. We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel

Joint factor analysis versus eigenchannels in speaker recognition

by Patrick Kenny, G. Boulianne, P. Ouellet, P. Dumouchel - IEEE Trans. Audio, Speech, Lang. Process , 2007
"... Abstract — We compare two approaches to the problem of session variability in GMM-based speaker verification, eigen-channels and joint factor analysis, on the NIST 2005 speaker recognition evaluation data. We show how the two approaches can be implemented using essentially the same software at all s ..."
Abstract - Cited by 115 (13 self) - Add to MetaCart
Abstract — We compare two approaches to the problem of session variability in GMM-based speaker verification, eigen-channels and joint factor analysis, on the NIST 2005 speaker recognition evaluation data. We show how the two approaches can be implemented using essentially the same software at all

R.: Non-negative matrix factorization based compensation of music for automatic speech recognition

by Bhiksha Raj, Tuomas Virtanen, Sourish Chaudhuri, Rita Singh - In: Proc. of Interspeech. Makuhari , 2010
"... This paper proposes to use non-negative matrix factorization based speech enhancement in robust automatic recognition of mixtures of speech and music. We represent magnitude spectra of noisy speech signals as the non-negative weighted linear combination of speech and noise spectral basis vectors, th ..."
Abstract - Cited by 34 (12 self) - Add to MetaCart
This paper proposes to use non-negative matrix factorization based speech enhancement in robust automatic recognition of mixtures of speech and music. We represent magnitude spectra of noisy speech signals as the non-negative weighted linear combination of speech and noise spectral basis vectors

Speaker and session variability in GMM-based speaker verification

by P. Kenny, G. Boulianne, P. Ouellet, P. Dumouchel - IEEE Trans. Audio, Speech, Lang. Process , 2007
"... Abstract — We present a corpus-based approach to speaker verification in which maximum likelihood II criteria are used to train a large scale generative model of speaker and session variability which we call joint factor analysis. Enrolling a target speaker consists in calculating the posterior dist ..."
Abstract - Cited by 55 (9 self) - Add to MetaCart
Abstract — We present a corpus-based approach to speaker verification in which maximum likelihood II criteria are used to train a large scale generative model of speaker and session variability which we call joint factor analysis. Enrolling a target speaker consists in calculating the posterior

Effect of temporal envelope smearing on speech reception.

by Rob Drullman , Joost M Festen , Reinier Plomp - International Journal of Bioelectromagnetism , 2011
"... The effect of smearing the temporal envelope on the speech-reception threshold (SRT) for sentences in noise and on phoneme identification was investigated for normal-hearing listeners. For this purpose, the speech signal was split up into a series of frequency bands (width of The ear's resolut ..."
Abstract - Cited by 145 (0 self) - Add to MetaCart
noise with the same temporal envelope as the speech waveform, Freyman et al. (1991) have shown that nonlinear amplification of the envelope (a 10-dB increase of the consonant portion) has no effect on overall consonant recognition, but it can alter confusion patterns for specific consonant groups

Speaker recognition with session variability normalization based on MLLR adaptation transforms”,

by Senior Member, IEEE Andreas Stolcke , Sachin S Kajarekar , Luciana Ferrer , Elizabeth Shriberg - IEEE Trans. Audio, Speech, and Lang. Process., , 2007
"... Abstract-We present a new modeling approach for speaker recognition that uses the maximum-likelihood linear regression (MLLR) adaptation transforms employed by a speech recognition system as features for support vector machine (SVM) speaker models. This approach is attractive because, unlike standa ..."
Abstract - Cited by 29 (7 self) - Add to MetaCart
algorithms for intersession variability compensation perform in conjunction with MLLR-SVM. Index Terms-Intersession variability compensation, maximum-likelihood linear regression-support vector machine (MLLR-SVM), speaker recognition.

i-vector based speaker recognition on short utterances

by Ahilan Kanagasundaram, Robbie Vogt, David Dean, Sridha Sridharan, Michael Mason - in Interspeech 2011 , 2011
"... Robust speaker verification on short utterances remains a key consideration when deploying automatic speaker recognition, as many real world applications often have access to only lim-ited duration speech data. This paper explores how the recent technologies focused around total variability modeling ..."
Abstract - Cited by 28 (5 self) - Add to MetaCart
Robust speaker verification on short utterances remains a key consideration when deploying automatic speaker recognition, as many real world applications often have access to only lim-ited duration speech data. This paper explores how the recent technologies focused around total variability

Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis

by Douglas Sturim, Pedro Torres-carrasquillo, Thomas F. Quatieri, Nicolas Malyska, Alan Mccree - Proceedings of Interspeech , 2011
"... Abstract 1 Of increasing importance in the civilian and military population is the recognition of Major Depressive Disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we investigate automatic class ..."
Abstract - Cited by 6 (3 self) - Add to MetaCart
Abstract 1 Of increasing importance in the civilian and military population is the recognition of Major Depressive Disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we investigate automatic
Results 1 - 10 of 279