Spectral tilt estimation for speech intelligibility enhancement using RNN based on all-pole model

Abstract

Speech intelligibility enhancement is extremely meaningful for successful speech communication in noisy environments. Several methods based on Lombard effect are used to increase intelligibility. In those methods, spectral tilt has been suggested to be a significant characteristic to produce Lombard speech that is more intelligible than normal speech. All-pole model computed by some methods has been used to capture the accurate spectral tilt of high-quality speech, but they are not appropriate for the spectral tilt estimation of telephone speech. In this paper, recurrent neural networks (RNNs) are used to estimate the tilt of telephone speech in German and English. RNN-based spectral tilt estimation show the robustness on the change of the all-pole model order and phonation type for narrow and wideband speech. Mean squared error (MSE) of spectral tilt estimation using RNN-based method is increased by about 26.20% in narrow speech and 19.49% in wideband speech comparing to the DNN-based measure.