Acoustic compensation methods for body transmitted speech conversion

Statistical voice conversion is very effective for enhancing body transmitted speech recorded with Non-Audible Murmur (NAM) microphone. In this method, a probabilistic model to convert body transmitted speech into natural speech is trained previously. Because acoustic characteristics of body transmitted speech is sensitive to recording conditions such as a location of NAM microphone, significant degradation of the conversion performance is often caused in practical situations by acoustic mismatches between training and conversion processes. To alleviate this problem, we propose unsupervised acoustic compensation methods for body transmitted voice conversion. Experimental results demonstrate that the proposed methods significantly reduce the quality degradation of converted speech caused by the acoustic mismatches.