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An enhanced Automatic Speech Recognition (ASR) system based on Hidden Markov Models (HMM) is presented. The system makes use of two sparse algorithms in order to remove the noise from the speech signal and improve the overall ASR recognition rate: Robust Principal Component Analysis (RPCA) and Exemplar-based Sparse Representation. We start with the premise that RPCA offers better results at lower...
This paper presents a way of improving the recognition rate of a typical Hidden Markov Model (HMM) -based Automatic Speech Recognition (ASR) system by integrating the l1 - least absolute deviation (LAD) algorithm and the l0 - least square (LS) algorithm in a framework designed to selectively use them based on the level of impulse noise present in speech signal. We present the overall architecture...
This paper considers the problem of resource-constrained and noise-limited localization and estimation of dynamic targets that are sparsely distributed over a large area. We generalize an existing framework [Bashan <etal/>, 2008] for adaptive allocation of sensing resources to the dynamic case, accounting for time-varying target behavior such as transitions to neighboring cells and varying amplitudes...
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