Speech Dereverberation Based on Convex Optimization Algorithms for Group Sparse Linear Prediction

In this paper, we consider methods for improving far-field speech
recognition using dereverberation based on sparse multi-channel linear
prediction. In particular, we extend successful methods based on nonconvex iteratively reweighted least squares, that look for a sparse
desired speech signal in the short-term Fourier transform domain, by proposing sparsity promoting convex functions. Furthermore, we show how to improve performance by applying regularization into both the reweighted least squares and convex methods. We evaluate
the methods using large scale simulations by mimicking the application scenarios of interest. The experiments show that the proposed
convex formulations and regularization offer improvements over existing methods with added robustness and flexibility in fairly different
acoustic scenarios.