Nonaudible murmur enhancement based on statistical voice conversion and noise suppression with external noise monitoring

This paper presents a method for making nonaudible murmur (NAM) enhancement based on statistical voice conversion (VC) robust against external noise. NAM, which is an extremely soft whispered voice, is a promising medium for silent speech communication thanks to its faint volume. Although such a soft voice can still be detected with a special body-conductive microphone, its quality significantly degrades compared to that of air-conductive voices. It has been shown that the statistical VC technique is capable of significantly improving quality of NAM by converting it into the air-conductive voices. However, this technique is not helpful under noisy conditions because a detected NAM signal easily suffers from external noise, and acoustic mismatches are caused between such a noisy NAM signal and a previously trained conversion model. To address this issue, in this paper we apply our proposed noise suppression method based on external noise monitoring to the statistical NAM enhancement. Moreover, a known noise superimposition method is further applied in order to alleviate the effects of residual noise components on the conversion accuracy. The experimental results demonstrate that the proposed method yields significant improvements in the conversion accuracy compared to the conventional method.