In practical applications, when testing parametric restrictions for hidden Markov models (HMMs), one frequently encounters non-standard situations such as testing for zero entries in the transition matrix, one-sided tests for the parameters of the transition matrix or for the components of the stationary distribution of the underlyingMarkov chain, or testing boundary restrictions on the paramet...

In this paper we investigate the use of voice activity detection (VAD) for improving noise models used for cepstral domain minimum mean squared error (MMSE) filtering of noisy speech. Due to the popularity of MFCC features for speech recognition, it is useful to have VAD methods and MMSE filtering algorithms that both work in the MFCC domain. We propose a method for VAD based on the likelihood ...

This paper develops a newmultiuser detector based on a network of kalman filters (NKF) dealing withmultiple-access interference (MAI), intersymbol interference (ISI), and an impulsive observation noise. The two proposed schemes are based on the modeling of the DS-CDMA system by a discrete-time linear system that has non-Gaussian state and measurement noises. By approximating the non-Gaussian de...

Introduction: Voice activity detection (VAD) remains a challenging problem in speech processing and affects a number of applications including noise reduction for digital hearing aid devices, speech recognition systems and speech coding for discontinuous speech transmission (DTX) in mobile and IP networks. During the last decade, researchers have paid attention to the study of discriminative fe...

In this paper, we consider the problem of estimating and testing a general linear hypothesis in a general multivariate linear model, the so called Growth Curve model, when the p×N observation matrix is normally distributed with an unknown covariance matrix. The maximum likelihood estimator (MLE) for the mean is a weighted estimator with the inverse of the sample covariance matrix which is unsta...

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modula...

The increasing availability of large-scale trajectory data provides us great opportunity to explore them for knowledge discovery in transportation systems using advanced data mining techniques. Nowadays, large number of taxicabs in major metropolitan cities are equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act...

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modula...

It has been shown that in optical communication systems with detectors that have a multiplicative noise component, the bit error rate (BER) can be improved by orders of magnitude through the use of a thresholding scheme based on a likelihood ratio test (LRT) instead of a matched filter [1]. An LRT detection scheme of this type has been derived previously for optically encoded Manchester data [1...