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A throat microphone (TM) produces speech which is perceptually poorer than that produced by a close speaking microphone (CSM) speech. Many attempts at improving the quality of TM speech have been made by mapping the features corresponding to the vocal tract system. These techniques are limited by the methods used to generate the excitation signal. In this paper a method to map the source (excitation) using multilayer feed-forward neural networks is proposed for voiced segments. This method anchors the analysis windows at the regions around the instants of glottal closure, so that the non-linear characteristics in these region of TM and CSM microphone is emphasized in the mapping process. The features obtained from these regions for both TM and CSM speech are used to train a MLFFNN to capture the non-linear relation between them. An improved technique for mapping the system features is also proposed. Speech synthesized using the proposed techniques was evaluated through subjective tests and was found to be significantly better than TM speech.