  Learning to respond to voice-text input involves the subject's ability in
understanding the phonetic and text based contents and his/her ability to
communicate based on his/her experience. The neuro-cognitive facility of the
subject has to support two important domains in order to make the learning
process complete. In many cases, though the understanding is complete, the
response is partial. This is one valid reason why we need to support the
information from the subject with scalable techniques such as Natural Language
Processing (NLP) for abstraction of the contents from the output. This paper
explores the feasibility of using NLP modules interlaced with Neural Networks
to perform the required task in autogenic training related to medical
applications.
