Model-based speech enhancement using a bone-conducted signal

Abstract

Codebook-based single-microphone noise suppressors, which exploit prior knowledge about speech and noise statistics, provide better performance in nonstationary noise. However, as the enhancement involves a joint optimization over speech and noise codebooks, this results in high computational complexity. A codebook-based method is proposed that uses a reference signal observed by a bone-conduction microphone, and a mapping between air- and bone-conduction codebook entries generated during an offline training phase. A smaller subset of air-conducted speech codebook entries that accurately models the clean speech signal is selected using this reference signal. Experiments support the expected improvement in performance at low computational complexity.