A discriminative post-filter for speech enhancement in hearing aids

Abstract

For hearing aid (HA) devices, speech enhancement (SE) is an essential unit aiming to improve signal-to-noise ratio (SNR) and quality of speech signals. Previous studies, however, indicated that user experience with current HAs was not fully satisfactory in noisy environments, suggesting that there is still room for improvement of SE in HA devices. This study proposes a novel discriminative post-filter (DPF) approach to further enhance the SNR and quality of SE processed speech signals. The DPF uses a filter to increase the energy contrast (discrimination) of speech and noise segments in a noisy utterance. In this way, SNR and sound quality of speech signals can be improved, and annoying musical noises can be suppressed. To verify the effectiveness of DPF, the present study integrates DPF with a previously proposed generalized maximum a posteriori spectral amplitude estimation (GMAPA) SE method. Experimental results demonstrated that when comparing to GMAPA alone, this integration can further improve output SNR and perceptual evaluation of speech quality (PESQ) scores and effectively suppress musical noises across various noisy conditions. Due to its low-complexity, low-latency, and high-performance, DPF can be suitably integrated in HA devices, where computational efficiency, power consumption, and effectiveness are major considerations.