Speaker-Independent Spectral Enhancement for Bone-Conducted Speech

Abstract

Because of the acoustic characteristics of bone-conducted (BC) speech, BC speech can be enhanced to better communicate in a complex environment with high noise. Existing BC speech enhancement models have weak spectral recovery capability for the high-frequency part of BC speech and have poor enhancement and robustness for the speaker-independent BC speech datasets. To improve the enhancement effect of BC speech for speaker-independent speech enhancement, we use a GANs method to establish the feature mapping between BC and air-conducted (AC) speech to recover the missing components of BC speech. In addition, the method adds the training of the spectral distance constraint model and, finally, uses the enhanced model completed by the training to reconstruct the BC speech. The experimental results show that this method is superior to the comparison methods such as CycleGAN, BLSTM, GMM, and StarGAN in terms of speaker-independent BC speech enhancement and can obtain higher subjective and objective evaluation results of enhanced BC speech.