MODULATION WIENER FILTER FOR IMPROVING SPEECH INTELLIGIBILITY

Abstract

This paper presents a single-channel high-dimensional Wiener filter in the spectro-temporal modulation domain. Unlike other conventional noise reduction techniques, the proposed algorithm not only reduces noise but also enhances the "textures" of the speech signal. A non-iterative decision-directed noise estimation method is adopted to estimate the modulation SNR for the modulation-domain Wiener filter. The efficacy of the proposed algorithm in enhancing speech intelligibility is assessed using the short-time objective intelligibility (STOI) measure. Statistical analysis results demonstrate that our proposed algorithm can improve STOI scores in speech-shape noise (SSN) and white noise conditions, but not in babble noise condition, while the conventional Wiener filter fails to improve STOI scores in all three noise conditions.