Word endpoint detection using dynamic programming

The paper deals with the use of dynamic programming for word endpoint detection in isolated word recognition. Endpoint detection is based on likelihood maximization. Expectation maximization approach is used to deal with the problem of unknown parameters. Speech signal and background noise energy is used as features for making decision. Performance of the proposed approach was evaluated using isolated Lithuanian words speech corpus.