Multisensory speech enhancement in noisy environments using bone-conducted and air-conducted microphones

In this paper, we propose a speech enhancement algorithm for estimating the clean speech using samples of air-conducted and bone-conducted speech signals. We introduce a model in a supervised learning framework by approximating a mapping from concatenation of noisy air-conducted and bone-conducted speech to clean speech in the short time Fourier transform domain. Two function extension schemes are utilized: geometric harmonics and Laplacian pyramid. Performances obtained from the two schemes are evaluated and compared in terms of spectrograms and log spectral distance measures.