An evaluation of speech reconstruction for degraded speech of optical microphone with deep neural network

Measuring distant-talking speech with high accuracy is important in detecting criminal activity. Various microphones such as the parabolic and shotgun microphones have been developed for measuring it. However, most of them have difficulty in extracting distant-talking speech at a target position if they are surrounded by noisy sound sources. Therefore, this study focuses on a microphone system to extract the distant-talking speech by vibrating a papery object using laser light. This system is referred to as an optical microphone in this study. The sound quality of the optical microphone is especially degraded at higher frequencies because it utilizes an external diaphragm consisting of various materials as the vibrating papery object. In this study, we therefore propose a reconstruction method for degraded distant-talking speech observed with the optical microphone. The method is realized by using a deep neural network (DNN) that is trained as the system between clean and observed speech signals. In the proposed method, the log-power spectra of 11 frames are used for the input of the DNN. Finally, we confirmed the effectiveness of the proposed system through an evaluation experiment.