A Robust Method for Blindly Estimating Speech Transmission Index using Convolutional Neural Network with Temporal Amplitude Envelope
Suradej Duangpummet, Jessada Karnjana, Waree Kongprawechnon, Masashi Unoki · 2019
We have developed a robust scheme for blindly estimating the speech transmission index (STI) based on a convolutional neural network (CNN) with temporal amplitude envelope as features. When assessing the quality of acoustics in a room where there are people present, STI needs to be estimated without measuring the room impulse response (RIR) or using a modulation transfer function (MTF). This estimation can be problematic because a blind method based on the MTF has low accuracy when the stochastic models of RIR and the background noise are mismatched to real sound environments. We improve the accuracy of STI estimation in noisy reverberant spaces by using a CNN that takes the entire temporal amplitude envelope of an observed speech signal as its input. Simulations were performed to evaluate the proposed scheme and results showed that it can maintain the appropriate accuracy under various realistic room acoustic conditions with an average RMSE of 0.12 and correlation of 0.87. These results demonstrate that the proposed scheme can robustly and blindly estimate STIs in noisy reverberant environments.