Abstract
A bone conduction microphone detects a vibration of a bone, e.g., jawbone. It is useful for communication in extremely noisy places such as in an engine room in a ship or on an airfield, because it has a strong resistance to noise in the air. However, its tone quality is bad. The aim of this research is to improve the quality of a bone conduction voice as much as possible to the quality of an air conduction voice.
In this report, we propose a novel voice conversion method from a bone conduction voice to an air conduction voice. The proposed method employs a neural gas network to perform a uniform quantization, and uses local linear conversion models from bone to air conduction voices, to realize a stable conversion performance.
The validity and the effectiveness of the proposed method have been verified by applying it to the tone quality improvement of the real bone conduction voice.
