A Speech Detection Method Based on Sparse Representation in Low SNR Environments

The research difficulties of speech detection in the present focus on the cases that the signal-to-noise ratio(SNR) is low and the background noise changes dramatically. For the problem of speech detection under low SNR environments, based on the sparsity of speech in frequency domain and the sparse representation ability in frequency domain of the over-complete Fourier basis, the speech signal is reconstructed with Matching Pursuit algorithm, and we propose a low SNR speech detection method which uses the short time energy of the reconstructed signal as a detection feature. The experimental results show that this algorithm exhibits higher robustness in the low SNR white noise environments.