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ȯɽÂêÌÜ¡§ Improving NAM Recognition Using Normal Speech Data
ȯɽ³µÍס§ A new type of microphones are able to capture faint voices directly from the skin of the speaker. The voice captured is called Non-Audible Murmur (NAM), and as a consequence, these microphones are called NAM microphones. By employing NAM as a way of communication, we can preserve better the privacy of conversation regardless of the listener (human or machine). Recently NAM is being considered as a new way of interaction with speech recognition systems. However, the lack of of training data has pushed researchers to develop ways of using already accumulated training data of ordinary speech. In this paper we present the results of investigating new methods in using ordinary speech data for building a NAM acoustic model. These methods are based on Speaker Adaptive Training (SAT) technique, and Constrained MLLR (Maximum Likelihood Linear Regression) to transform ordinary speech acoustic features into NAM features. The results produced by these methods yield an absolute increase of 2% in word accuracy compared with conventional methods.
 
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