  Compared with air-conducted speech, bone-conducted speech has the unique
advantage of shielding background noise. Enhancement of bone-conducted speech
helps to improve its quality and intelligibility. In this paper, a novel
CycleGAN with dual adversarial loss (CycleGAN-DAL) is proposed for
bone-conducted speech enhancement. The proposed method uses an adversarial loss
and a cycle-consistent loss simultaneously to learn forward and cyclic mapping,
in which the adversarial loss is replaced with the classification adversarial
loss and the defect adversarial loss to consolidate the forward mapping.
Compared with conventional baseline methods, it can learn feature mapping
between bone-conducted speech and target speech without additional
air-conducted speech assistance. Moreover, the proposed method also avoids the
oversmooth problem which is occurred commonly in conventional statistical based
models. Experimental results show that the proposed method outperforms baseline
methods such as CycleGAN, GMM, and BLSTM. Keywords: Bone-conducted speech
enhancement, dual adversarial loss, Parallel CycleGAN, high frequency speech
reconstruction
