A novel approach to supervised dimensionality reduction is introduced, based on Gaussian Restricted Boltzmann Machines. The proposed model should be considered as the analogue of the probabilistic LDA, using undirected graphical models. The training algorithm of the model is presented while its close relation to the cosine distance is underlined. For the problem of speaker verification, we appl...

This paper presents a probabilistic graphical model to formulate and deal with video-based face recognition. Our formulation divides the problem into two parts: one for likelihood measure and the other for transition measure. The likelihood measure can be regarded as a traditional task of face recognition within a single image, i.e., to recognize who the current observing face image is. In our ...

High-quality still-to-still (image-to-image) face authentication has shown success under controlled conditions inmany safety applications. However, video-to-video face authentication is still challenging due to appearance variations caused by pose changes. In this paper, we propose a video-to-video face authentication system that is robust to pose variations by making use of synthesized frontal...

In perceptive interface technologies used in smart-room environments, the determination of speech activity is one of key objectives. Due to the presence of environmental noises and reverberation, a robust Speech Activity Detection (SAD) system is required. In a previous work, a SAD system, which used Linear Discriminant Analysis-extracted features and a Decision Tree classifier, was successfull...

We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible curve-models to each class of complex-shaped curves presenting regime changes. The model parameters are learned by maximizing the observed-data log-likelihood for each class by using a dedicated expectation-maximizatio...

If one could predict which of two classifiers will correctly classify a particular sample, then one could use the better classifier. Continuing this selection process throughout the data set should result in improved accuracy over either classifier alone. Fortunately, scalar measures which relate to the degree of confidence that we have in a classification can be computed for most common classi...

To improve speech recognition performance, acoustic feature transformation based on discriminant analysis has been widely used. For the same purpose, discriminative training of HMMs has also been used. In this letter we investigate the effectiveness of these two techniques and their combination. We also investigate the robustness of matched and mismatched noise conditions between training and e...

The amount of training data has a crucial effect on the accuracy of HMM based meeting recognition systems. Conversational telephone speech matches speech in meetings well. However it is naturally recorded with low bandwidth. In this paper we present a scheme that allows to transform wide-band meeting data into the same space for improved model training. The transformation into a joint space all...

In this paper, two novel features, Line Spectrum Center Range and Line Spectrum Flux, both derived from Line Spectrum Frequencies, are proposed to detect the presence of speech in various acoustic environments. Evaluation results using Fischer Discriminant Analysis and Scatter Matrices indicated that the new features excel the state-of-theart features. An environmental robust hybrid feature set...

Linear spectral mixture analysis has been widely used for subpixel detection and mixed pixel classification. When it is implemented as constrained LSMA, the constraints are generally imposed on abundance fractions in the mixture. In this paper, we consider an alternative approach, which imposes constraints on target signature vectors rather than target abundance fractions. The idea is to constr...