Pitch-Synchronous Peak-Amplitude (PS-PA)-Based Feature Extraction Method for Noise-Robust ASR

Abstract

A novel pitch-synchronous auditory-based feature extraction method for robust automatic speech recognition (ASR) is proposed. A pitch-synchronous zero-crossing peak-amplitude (PS-ZCPA)-based feature extraction method was proposed previously and it showed improved performances except when modulation enhancement was integrated with Wiener filter (WF)-based noise reduction and auditory masking. However, since zero-crossing is not an auditory event, we propose a new pitch-synchronous peak-amplitude (PS-PA)-based method to render the feature extractor of ASR more auditory-like. We also examine the effects of WF-based noise reduction, modulation enhancement, and auditory masking in the proposed PS-PA method using the Aurora-2J database. The experimental results show superiority of the proposed method over the PS-ZCPA and other conventional methods. Furthermore, the problem due to the reconstruction of zero-crossings from a modulated envelope is eliminated. The experimental results also show the superiority of PS over PA in terms of the robustness of ASR, though PS and PA lead to significant improvement when applied together.