An Analysis of Noise-aware Features in Combination with the Size and Diversity of Training Data for DNN-based Speech Enhancement

Abstract

In this work, the generalization of speech enhancement algorithms based on deep neural networks (DNNs) for training datasets that differ in size and diversity is analyzed. For this, we compare noise aware training (NAT) features and signal-to-noise ratio (SNR) based noise aware training (SNR-NAT) features. NAT appends an estimate of the noise power spectral density (PSD) to a noisy periodogram input feature, whereas SNR-NAT uses the noise PSD for normalization. We show that the Hu noise corpus (limited size) and the CHiME 3 noise corpus (limited diversity) may result in DNNs which do not generalize well to unseen noises. We construct a large and diverse dataset from freely available data and show that it helps DNNs to generalize. However, we also show that with SNR-NAT features, the trained models are more robust even if a small or less diverse training set is employed. Using t-distributed stochastic neighbor embedding (t-SNE), we demonstrate that using SNR-NAT both the features and the resulting internal representation of the DNN are less dependent on the background noise which facilitates the generalization to unseen noise types.