Near and Far Field Speech-in-Noise Intelligibility Improvements Based on a Time–Frequency Energy Reallocation Approach

Abstract

An algorithm designed to enhance the intelligibility of speech signals before they are presented in noisy environments was evaluated. The processed and unprocessed speech had the same root-mean square level. Spectral energy was redistributed to increase the signal-to-noise ratio (SNR) in the mid- and high-frequency bands, while the softer segments of speech were increased in level by applying time-domain dynamic range compression. Noise level adaptation was introduced to increase the subjective quality of signals at high SNRs. Evaluations were conducted both in near field (headphones) and in far field (outdoor) conditions using listeners with normal hearing and two types of background. The results showed: (a) In the near field test, the proposed algorithm yielded significant intelligibility improvements relative to the unprocessed speech for both stationary and nonstationary backgrounds; (b) In the far field test, the proposed algorithm increased the intelligibility of unprocessed speech by a factor of seven at 200-m distance from the sound source; and (c) When the background level did not alter intelligibility, listeners preferred the quality of the speech processed by the noise-dependent version of the algorithm.