Speech-in-noise intelligibility improvement based on spectral shaping and dynamic range compression

In this paper, we suggest a non-parametric way to improve the intelligibility of speech in noise. The signal is enhanced before presented in a noisy environment, under the constraint of equal global signal power before and after modifications. Two systems are combined in a cascade form to enhance the quality of the signal first in frequency (spectral shaping) and then in time (dynamic range compression). Experiments with speech shaped (SSN) and competing speaker (CS) types of noise at various low SNR values, show that the suggested approach outperforms state-of-the art methods in terms of the Speech Intelligibility Index (SII). In terms of SNR gain there is an improvement of 7 dB (SSN) and 8 dB (CS) over these methods. A formal listening test confirm the efficiency of the suggested system in enhancing speech intelligibility in noise.