In this paper, we introduce the Stethoscopic Non-Audible Murmur (NAM) microphone, and we focus on its application in automatic speech recognition systems. The NAM microphone is attached behind the talker's ear, and can capture very quietly uttered murmur (NAM speech). It is applicable in automatic speech recognition systems, when privacy is important in human-machine communication. Moreover, since the NAM microphone receives the speech signal directly from the body, it shows robustness against the environmental noises. In addition to these, it might be also used in special systems (speech recognition, speech transform, etc.) for sound-impaired people. By applying adaptation techniques, we performed automatic speech recognition experiments for NAM speech. Using Maximum A Posteriori (MAP) adaptation, and a combination with Maximum Likelihood Linear Regression (MLLR) adaptation we achieved for a 20k vocabulary dictation system a 93.5% word accuracy, which is a very promising result.