LAUSR

Abstract Voice recognition technology can almost accurately recognize a user’s voice in the absence of background noise or when the noise levels are extremely low. However, voice recognition in the presence of background noise with various voice signals is complicated. The problem in the current voice separation scheme is that though voice separation is possible from mixed voice signals, a permutation problem occurs that renders it difficult to identify the desired signal among the separated signals. In this paper, we propose a driver voice separation method for autonomous vehicles. To solve the permutation problem, active-beacon-based driver sound separation (ABDSS) utilizing active sound is used to distinguish the driver’s sound. After recording the voice work, simulation was performed. In the simulation, the proposed method succeeded in separating and distinguishing the original voice signals from the mixed voice signals. In addition, the coherence, kurtosis, and skewness calculation were used to verify that the separated signals were correctly identified in the simulation. Therefore, the proposed method is simpler in terms of hardware configuration than the existing methods and it is suitable for in-vehicle voice separation systems as well.