Separation of simultaneous word sequences using Markov model techniques
This thesis develops a method of separating multiple simultaneous conversations through the use of Markov Models. Text samples which represent the conversations to be used as training data are described by a grammar based upon word and word-pair occurences within the text. This grammar is then used to establish a Markov Model for the text. These models are then combined to form a Marjov Model which describes the simultaneous occurrence of multiple conversations. Artificially generated word sequences which have the same grammar as the training conversations are supplied as input to the conversation filter, whose purpose is to "listen to" one of the input sequences. The conversation filter takes on either an optimal form in which the grammars of all input sequences to the filter are known, or a sub-optimal form which uses only the grammar of the desired output.
Networked Digital Library of Theses and Dissertations
Monterey, California. Naval Postgraduate School