SPEECH INTELLIGIBILITY ENHANCEMENT BY EQUALIZATION FOR IN-CAR APPLICATIONS

In this paper, we propose a speech intelligibility enhancement method for typical in-car applications in noisy environments. While traditional speech enhancement algorithms aim at increasing the Signal to Noise Ratio (SNR), the goal here is to increase intelligibility by applying dedicated voice transformation techniques without changing the original SNR. The proposed method consists in an adaptive equalizer which reallocates the energy of frequency bands to maximize the Speech Intelligibility Index (SII) under the constraint of a fixed perceived loudness. The validation of the algorithm is carried out by means of a perceptual test derived from the Hearing in Noise Test (HINT) using four typical in-car noises of different driving conditions. The results obtained demonstrate the merit of the algorithm for low-frequency noises, that correspond to usual driving conditions, but also show the limit of the algorithm on noises with a spectrum more spread out induced by rain.