Improved Automatic Speech Recognition system by using compressed sensing signal reconstruction based on L0 and L1 estimation algorithms

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 l
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- least absolute deviation (LAD) algorithm and the l
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- 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 of the model, as well as experimental results and compare our enhanced noise-robust HMM-based ASR system with state-of-the-art proving the improvements brought by this approach as well as future directions of research.