Model-based integration of reverberation for noise-adaptive near-end listening enhancement

Speech intelligibility is an important factor for successful speech communication in today's society. So-called near-end listening enhancement (NELE) algorithms aim at improving speech intelligibility in conditions where the (clean) speech signal is accessible and can be modified prior to its presentation. However, many of these algorithms only consider the detrimental effect of noise and disregard the effect of reverberation. Therefore, in this paper we propose to additionally incorporate the detrimental effects of reverberation into noise-adaptive near-end listening enhancement algorithms. Based on the Speech Transmission Index (STI), which is widely used for speech intelligibility prediction, the effect of reverberation is effectively accounted for as an additional noise power term. This combined noise power term is used in a state-of-the-art noise-adaptive NELE algorithm. Simulations using two objective measures, the STI and the short-time objective intelligibility (STOI) measure demonstrate the potential of the proposed approach to improve the predicted speech intelligibility in noisy and reverberant conditions.