This work compares the accuracy of fundamental frequency and formant frequency estimation methods and maximum a posteriori (MAP) prediction from MFCC vectors with hand-corrected references. Five fundamental frequency estimation methods are compared to fundamental frequency prediction from MFCC vectors in both clean and noisy speech. Similarly, three formant frequency estimation and prediction m...

We describe a method of updating a hidden Markov model (HMM) for speaker verification using a small amount of new data for each speaker. The HMM is updated by adapting the model parameters to the new data by maximum a posteriori (MAP) estimation. The initial values of the a priori parameters in MAP estimation are set using training speech used for first creating a speaker HMM. We also present a...

The performance of speech recognition system will be significantly deteriorated because of the mismatches between training and testing conditions. This paper addresses the problem and proposes an algorithm to adapt the mean and covariance of HMM simultaneously within the minimum classification error linear regression (MCELR) framework. Rather than estimating the transformation parameters using ...

This study investigates a population decoding paradigm, in which the estimation of stimulus in the previous step is used as prior knowledge for consecutive decoding. We analyze the decoding accuracy of such a Bayesian decoder (Maximum a Posteriori Estimate), and show that it can be implemented by a biologically plausible recurrent network, where the prior knowledge of stimulus is conveyed by th...

This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori—Markov random field estimation is used...

This paper presents a channel estimation scheme for multiple-input multiple-output with orthogonal frequency-division multiplexing (MIMO-OFDM) in fast varying Rayleigh channels. To handle rapid variation of channels within a transmission block, we propose a novel maximum a posteriori probability-based channel estimation scheme using pilot symbols. With the estimate of the channel matrix for the...

This paper is concerned with maximum a posteriori state estimation of linear systems in the presence of constrained scalar process noise. The goal of this work is to investigate closed form solutions aimed at reducing the online computations required by the estimation problem. Dynamic programming is used to derive a closed form solution that can be precomputed offline. The optimal solution is g...

This paper introduces a blind method based on Bayesian maximum a posteriori estimation theory for restoring images corrupted by noise and blurred by one or more unknown point spread functions. Image and blur prior information is expressed in the form of parametric generalized Gauss Markov random field models. A method for estimating the GGMRF neighborhood influence parameters is presented, alon...

Scene understanding of remotely sensed images requires a certain amount of preprocessing in order to remove, or alleviate the effects of, all those factors that disturb the imaging process. These factors depend essentially on the peculiar way in which each kind of sensor acquires the image (sensorrelated factors) and on the terrain topography, the illumination and the view angle (radiometric fa...

Many machine learning problems deal with the estimation of conditional probabilities p(y | x) from data (x1, yi), . . . , (xn, yn). This includes classification, regression and density estimation. Given a prior for p(y | x) the maximum a-posteriori method estimates p(y | x) as the most likely probability given the data. This principle can be formulated rigorously using the Cameron-Martin theory...