# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 1 Introduction
Therefore, the difficulty of blind enhancement is to infer and recover high-frequency information based on limited information in the middle and low-frequency bands. At present, BC speech enhancement methods include traditional methods and depth learning methods. Traditional BC speech enhancement methods are based on the Gaussian mixture model, the least mean-square error method, and other statistical methods. The traditional blind BC speech enhancement method analyzes the spectrum characteristics of BC speech from many aspects, finds the correlation between AC and BC speech, and lays a good foundation for the follow-up work. Recently, with the rapid development of deep learning technology, it has been widely used in various fields. Compared with traditional methods, the method based on deep learning can better learn the spectral characteristics of bone and air conduction speech and obtain a better enhancement effect [7; 8; 9]. DNN is gradually applied to learning the nonlinear conversion relationship between spectral envelope features [9; 10]. Because the deep neural network has a good characterization ability for high-dimensional data, [PERSON] et al. have used the deep denoising autoencoder (DDAE) to model the transformational relation of the high-dimensional Mel-amplitude spectrum features [8]. [PERSON] et al. [7] regarded the BC speech spectrum as a two-dimensional image, and proposed a bidirectional long short-term memory (attention-based BLSTM, ABBLSTM) on the basis of using the structural similarity (SSIM) loss function and achieved certain results. At present, most neural network enhancement frameworks are based on short-time Fourier characteristics. Most of the research inputs the spectrum into the network and thinks that the short-term phase is not important for speech enhancement. However, [PERSON] et al. found through experiments that when the phase spectrum of the enhanced speech is effectively restored, the quality of speech enhancement will be significantly improved [11]. The BC speech enhancement method based on deep learning can infer high-frequency information by analyzing the low-frequency spectrum signal information composition of BC speech, reconstructing the full band speech, and improving the quality and intelligibility of the generated speech. However, the deep neural network currently used in BC speech enhancement methods is difficult to fully learn the characteristics of BC speech in the case of limited BC speech samples and is not robust to speaker-independent speech data sets. Recently, the authors of [12] used generative adversarial networks (GAN) to conduct in-depth learning and training of data through game learning of generators and discriminators. In theory, it has a strong generating ability. It has been widely used in many fields, such as image processing [13; 14], video generation, speech synthesis [15], and so on. In particular, it has achieved considerable success in generating realistic images and solving complex data distribution problems [12]. [PERSON] et al. [16] proposed a method that can reconstruct the high-frequency part of BC speech without using the dynamic integration algorithm (DTW) for feature alignment, and the enhanced BC speech is clear. Aiming at the problem of BC speech enhancement for speaker-independent systems, to pay full attention to the information of BC speech, we make full use of the existing features in the case of fewer training data. The BC speech enhancement generative adversarial networks for speaker-independent (BSEGAN-SI) method is proposed in this paper. In view of the shortcomings of the original GAN, this paper selects a reasonable generator and discriminator structure to build a BC speech blind enhancement architecture and directly learns the mapping relationship between BC speech and pure AC speech.