# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 1 Introduction  
In the daily communication process, the background noise of the surrounding environment has a great impact on it, especially on the noisy roadside, near construction sites, train waiting rooms, and other places. The military communication field also needs to remove all kinds of noises. Therefore, improving the voice quality is very important for improving the quality of life. Environmental noise not only affects voice communication in daily life but also needs to be removed in military communication, fire rescue communication, and other fields. In the field of military communications, many places need to use voice to transmit information, and voice quality is crucial. If the voice quality is poor, the transmitted information may not be obtained correctly, resulting in very significant losses. Speech enhancement is an important technical means of reducing noise interference in speech communication. At present, speech enhancement technology has made great progress. Traditional speech enhancement methods [1; 2] and depth-learning-based enhancement methods [3; 4] emerge endlessly, which can achieve good enhancement effects when dealing with stationary noise. However, when the noise environment is very complex, the effect of existing speech enhancement methods will be greatly reduced. Ordinary air conduction (AC) microphones transmit information by capturing voice signals in the air, so they are very vulnerable to various environmental noises. However, it is precisely because of the unique transmission channel of its speech signal that the speech signal captured by the bone-conducted (BC) equipment can completely shield the background noise, separate the noise from the transmitted speech information from the sound source, and prevent the noise from interfering with the speech signal. To fundamentally solve the problem of environmental noise [5], people began to use BC microphones for speech communication in some strong noise environments, such as military aircraft and tanks, where the background noise in the cockpit is very strong. In addition, the BC communication equipment is small in size, easy to carry, and can communicate normally in water. It can also be used as speech recognition under strong noise. Generally, the BC microphone with good universality mainly collects signals from sensors. The sensors are close to the skin, which can sense the vibration signals of the bones driven by human speech. Then, the collected signals are converted into formats to form BC speech signals. Different from traditional AC microphones, BC microphones only collect vibration information and will not be affected by background noise. They shield noise interference from the sound source to obtain relatively pure speech signals. This is also an important feature of BC microphones in practical applications and has important application value in complex noise environments. BC speech can effectively resist the interference of environmental noise; however, due to the change in the sound transmission path and the limitation of the human voice generation mechanism, the BC microphone will lose some signals, especially important high-frequency voice signal information, which makes the comfort experience of the BC microphone for users unfriendly. High-frequency information of BC speech is lost, some unvoiced syllables are missing, and the sense of hearing is dull and not clear enough, so the speech intelligibility is low, and it is difficult to directly apply to normal communication [5]. These shortcomings also seriously restrict the development of BC communication. Compared with the acoustic characteristics of AC speech, the biggest problem of BC speech is the loss of high-frequency information components. Different from the multi-sensor fusion enhancement algorithm, blind enhancement of BC speech only has BC speech information in the enhancement phase and does not need to be supplemented by AC speech features. At the same time, this process is called Blind Restoration [6].