An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech
This paper presents a novel postfiltering approach based on the locally linear embedding (LLE) algorithm for speech enchantment (SE). The aim of the proposed LLE-based postfiltering approach is to further remove the residual noise components from the SE-processed speech signals through a spectral conversion process, thereby increasing the signal-to-noise ratio (SNR) and speech quality. The proposed postfiltering approach consists of two phases. In the offline phase, paired SE-processed and clean speech exemplars are prepared for dictionary construction. In the online phase, the LLE algorithm is adopted to convert the SE-processed speech signals to the clean ones. The present study integrates the LLE-based postfiltering approach with a deep denoising autoencoder (DDAE) SE method, which has been confirmed to provide outstanding capability for noise reduction. Experimental results show that the proposed postfiltering approach can notably enhance the DDAE-based SE processed speech signals in different noise types and SNR levels.