Near-end listening enhancement using a noise-robust linear time-invariant filter

In environments with competing sound sources, speech intelligibility can be significantly compromised. This paper addresses the near-end listening enhancement (NELE) problem, i.e., the problem of processing an available clean speech signal in order to maximize its intelligibility when it is subsequently presented to a human listener in an adverse acoustic situation. We propose a time-invariant and low-complexity NELE algorithm that maximizes an approximation of the Speech Intelligibility Index by redistributing speech energy across frequency bands. Unlike existing algorithms, the proposed algorithm incorporates a mechanism that allows it to distinguish between temporally fluctuating and non-fluctuating noise maskers by using only long-term speech and noise statistics. Simulation results show that the proposed method outperforms baseline algorithms, whether time-invariant or time-varying, in a wide range of noise conditions.