Noise robust Automatic Speech Recognition system by integrating Robust Principal Component Analysis (RPCA) and Exemplar-based Sparse Representation

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 Signal-to-noise ratios (SNRs) while Exemplar-based Sparse Representation offers good results for SNRs lower than 15 dB, and therefore we envisage architecture able to select between the two algorithms depending on the SNR detected in the speech signal. We present the architecture of our proposed model, as well as the experimental results in different scenarios and the improvements that can be brought in future researches.