This paper presents a voice activity detection (VAD) scheme that is robust against noise, based on an optimally weighted combination of features. The scheme uses a weighted combination of four conventional VAD features: amplitude level, zero crossing rate, spectral information, and Gaussian mixture model likelihood. This combination in effect selects the optimal method depending on the noise co...

We suggest using a pair of metrics which quantify the extent to which the prior and likelihood functions influence inferences of parameters within a parametric Bayesian model, one of which is closely related to the reference prior of Berger and Bernardo. Our hope is that the utilization of these metrics will allow for the precise quantification of prior and likelihood information and mitigate t...

Andrew R. Barron Department of Statistics Yale University P.O. Box 208290 New Haven, CT 06520 Andrew. Barron@yale. edu Gaussian mixtures (or so-called radial basis function networks) for density estimation provide a natural counterpart to sigmoidal neural networks for function fitting and approximation. In both cases, it is possible to give simple expressions for the iterative improvement of pe...

Based on sampling likelihood and feature intensity, in this paper, a feature-preserving denoising algorithm for point-sampled surfaces is proposed. In terms of moving least squares surface, the sampling likelihood for each point on point-sampled surfaces is computed, which measures the probability that a 3D point is located on the sampled surface. Based on the normal tensor voting, the feature ...

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wireless sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements i...

Combination functions typically used in biometric identification systems consider as input parameters only those matching scores which are related to a single person in order to derive a combined score for that person. We discuss how such methods can be extended to utilize the matching scores corresponding to all persons. The proposed combination methods account for dependencies between scores ...

Based on sampling likelihood and feature intensity, in this paper, a feature-preserving denoising algorithm for point-sampled surfaces is proposed. In terms of moving least squares surface, the sampling likelihood for each point on point-sampled surfaces is computed, which measures the probability that a 3D point is located on the sampled surface. Based on the normal tensor voting, the feature ...

In this paper, we propose a new method, where the likelihood normalization technique is applied at both the frame and utterance levels. In this method based on Gaussian Mixture Models (GMM), every frame of the test utterance is inputed to the claimed and all background speaker models in parallel. In this procedure, for each frame, likelihoods from all the background models are available, hence ...

Abstract We consider likelihood score-based methods for causal discovery in structural models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification terms non-Gaussian error distribution. present a surprising negative result combination with nonparametric regression methods.