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33
Automatic Person Verification Using Speech and Face Information
, 2003
"... Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one pre ..."
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Cited by 38 (7 self)
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Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person's speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & checkin, as well as forensic work and law enforcement applications. Biometric systems
On adaptive decision rules and decision parameter adaptation for automatic speech recognition
 Proc. IEEE
, 2000
"... Recent advances in automatic speech recognition are accomplished by designing a plugin maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and ..."
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Cited by 35 (4 self)
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Recent advances in automatic speech recognition are accomplished by designing a plugin maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximumlikelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for highperformance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine
Online adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate
 IEEE Trans. Speech Audio Processing
, 1997
"... Online adaptive learning of the continuous density hidden Markov model based on approximate recursive ..."
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Cited by 34 (12 self)
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Online adaptive learning of the continuous density hidden Markov model based on approximate recursive
Robust speech recognition based on Bayesian prediction approach”, submitted to
 IEEE Trans. on Speech and Audio Process ing
, 1997
"... recognition problem in which mismatches exist between training and testing conditions, and no accurate knowledge of the mismatch mechanism is available. The only available information is the test data along with a set of pretrained Gaussian mixture continuous density hidden Markov models (CDHMM’s). ..."
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Cited by 29 (7 self)
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recognition problem in which mismatches exist between training and testing conditions, and no accurate knowledge of the mismatch mechanism is available. The only available information is the test data along with a set of pretrained Gaussian mixture continuous density hidden Markov models (CDHMM’s). We investigate the problem from the viewpoint of Bayesian prediction. A simple prior distribution, namely constrained uniform distribution, is adopted to characterize the uncertainty of the mean vectors of the CDHMM’s. Two methods, namely a model compensation technique based on Bayesian predictive density and a robust decision strategy called Viterbi Bayesian predictive classification are studied. The proposed methods are compared with the conventional Viterbi decoding algorithm in speakerindependent recognition experiments on isolated digits and TI connected digit strings (TIDIGITS), where the mismatches between training and testing conditions are caused by: 1) additive Gaussian white noise, 2) each of 25 types of actual additive ambient noises, and 3) gender difference. The experimental results show that the adopted prior distribution and the proposed techniques help to improve the performance robustness under the examined mismatch conditions. Index Terms—Bayesian predictive classification, minimax decision, plugin maximum a posteriori decision, predictive density, Viterbi Bayesian predictive classification. I.
A bayesian predictive classification approach to robust speech recognition
 in Proc. ICASSP97
, 1997
"... Abstract—We introduce a new decision strategy called Bayesian predictive classification (BPC) for robust speech recognition where unknown mismatch between training and testing conditions exists. We then propose and focus on one of the approximate BPC approaches called quasiBayes predictive class ..."
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Cited by 25 (4 self)
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Abstract—We introduce a new decision strategy called Bayesian predictive classification (BPC) for robust speech recognition where unknown mismatch between training and testing conditions exists. We then propose and focus on one of the approximate BPC approaches called quasiBayes predictive classification (QBPC). In a series of comparative experiments where the mismatch is caused by additive white Gaussian noise, we show that the proposed QBPC approach achieves a considerable improvement over the conventional plugin MAP decision rule. Index Terms—Bayesian predictive classification (BPC), plugin maximum a posteriori (MAP) decision, quasiBayes approximation, robust automatic speech recognition. I.
Online adaptive learning of the correlated continuous density hidden markov models for speech recog
 IEEE Trans. Speech Audio Processing
, 1999
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Using SelfOrganizing Maps and Learning Vector Quantization for Mixture Density Hidden Markov Models
, 1997
"... This work presents experiments to recognize pattern sequences using hidden Markov models (HMMs). The pattern sequences in the experiments are computed from speech signals and the recognition task is to decode the corresponding phoneme sequences. The training of the HMMs of the phonemes using the col ..."
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Cited by 22 (9 self)
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This work presents experiments to recognize pattern sequences using hidden Markov models (HMMs). The pattern sequences in the experiments are computed from speech signals and the recognition task is to decode the corresponding phoneme sequences. The training of the HMMs of the phonemes using the collected speech samples is a difficult task because of the natural variation in the speech. Two neural computing paradigms, the SelfOrganizing Map (SOM) and the Learning Vector Quantization (LVQ) are used in the experiments to improve the recognition performance of the models. A HMM consists of sequential states which are trained to model the feature changes in the signal produced during the modeled process. The output densities applied in this work are mixtures of Gaussian density functions. SOMs are applied to initialize and train the mixtures to give a smooth and faithful presentation of the feature vector space defined by the corresponding training samples. The SOM maps similar feature vect...
Online Bayesian Estimation of Transition Probabilities for Markovian Jump Systems
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2004
"... Markovian jump systems (MJSs) evolve in a jumpwise manner by switching among simpler models, according to a finite Markov chain, whose parameters are commonly assumed known. This paper addresses the problem of state estimation of MJS with unknown transition probability matrix (TPM) of the embedded ..."
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Cited by 22 (2 self)
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Markovian jump systems (MJSs) evolve in a jumpwise manner by switching among simpler models, according to a finite Markov chain, whose parameters are commonly assumed known. This paper addresses the problem of state estimation of MJS with unknown transition probability matrix (TPM) of the embedded Markov chain governing the jumps. Under the assumption of a timeinvariant but random TPM, an approximate recursion for the TPMs posterior probability density function (PDF) within the Bayesian framework is obtained. Based on this recursion, four algorithms for online minimum meansquare error (MMSE) estimation of the TPM are derived. The first algorithm (for the case of a twostate Markov chain) computes the MMSE estimate exactly, if the likelihood of the TPM is linear in the transition probabilities. Its computational load is, however, increasing with the data length. To limit the computational cost, three alternative algorithms are further developed based on different approximation techniquestruncation of high order moments, quasiBayesian approximation, and numerical integration, respectively. The proposed
Temporal and spatial data mining with secondorder hidden markov models
 Sigayret (eds), Fourth International Conference on Knowledge Discovery and Discrete Mathematics  Journées de l’informatique Messine  JIM’2003
, 2003
"... In the frame of designing a knowledge discovery system, we have developed stochastic models based on highorder hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the n previous states according to the ord ..."
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Cited by 14 (2 self)
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In the frame of designing a knowledge discovery system, we have developed stochastic models based on highorder hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the n previous states according to the order of the model. We study the process of achieving information extraction from spatial and temporal data by means of an unsupervised classification. We use therefore a French national database related to the land use of a region, namedTer Uti, which describes the land use both in the spatial and temporal domain. Landuse categories (wheat, corn, forest,...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies. The temporal segmentation of the data is done by means of a secondorder Hidden Markov Model (HMM2) that appears to have very good capabilities to locate stationary segments, as shown in our previous work in speech recognition. The spatial classification is performed by defining a fractal scanning of the images with the help of a HilbertPeano curve that introduces a total order on the sites, preserving the relation of neighborhood between the sites. We show that the HMM2 performs a classification that is meaningful for the agronomists. Spatial and temporal classification may be achieved simultaneously by means of a 2 levels HMM2 that measures the a posteriori probability to map a temporal sequence of images onto a set of hidden classes.
Enhancements to TransformationBased Speaker Adaptation: Principal Component and InterClass Maximum Likelihood Linear Regression
, 2000
"... iii Abstract In this thesis we improve speech recognition accuracy by obtaining better estimation of linear transformation functions with a small amount of adaptation data in speaker adaptation. The major contributions of this thesis are the developments of two new adaptation algorithms to improve ..."
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Cited by 7 (1 self)
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iii Abstract In this thesis we improve speech recognition accuracy by obtaining better estimation of linear transformation functions with a small amount of adaptation data in speaker adaptation. The major contributions of this thesis are the developments of two new adaptation algorithms to improve maximum likelihood linear regression. The first one is called principal component MLLR (PCMLLR), and it reduces the variance of the estimate of the MLLR matrix using principal component analysis. The second one is called interclass MLLR, and it utilizes relationships among different transformation functions to achieve more reliable estimates of MLLR parameters across multiple classes. The main idea of PCMLLR is that if we estimate the MLLR matrix in the eigendomain, the variances of the components of the estimates are inversely proportional to their eigenvalues. Therefore we can select more reliable components to reduce the variances of the resulting estimates and to improve speech recognition accuracy. PCMLLR eliminates highly variable components and chooses the principal components corresponding to the largest eigenvalues. If all the component are used, PCMLLR becomes the same as conventional MLLR. Choosing fewer principal components increases the bias of the estimates which can reduce recognition accuracy. To compensate for this problem, we developed weighted principal component MLLR (WPCMLLR). Instead of eliminating some of the components, all the components in WPCMLLR are used after applying weights that minimize the mean square error. The component corresponding to a larger eigenvalue has a larger weight than the component corresponding to a smaller eigenvalue. As more adaptation data become available, the benefits from these methods may become smaller because ...