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Recent advances in neural network (NN)-based speech enhancement schemes are shown to outperform most conventional techniques. However, the performance of such systems in adverse listening conditions such as negative signal-to-noise ratios and unseen noises is still far from that of humans. Motivated by the remarkable performance of humans under these challenging conditions, this paper investigates whether biophysicallyinspired features can mitigate the poor generalization capabilities of NN-based speech enhancement systems. We make use of features derived from several human auditory periphery models for training a speech enhancement system that employs long short-term memory (LSTM), and evaluate them on a variety of mismatched testing conditions. The results reveal that biophysically-inspired auditory models such as nonlinear transmission line models improve the generalizability of LSTMbased noise suppression systems in terms of various objective quality measures, suggesting that such features lead to robust speech representations that are less sensitive to the noise type.

Biophysically-inspired Features Improve the Generalizability of Neural Network-based Speech Enhancement Systems