Comparison of energy-based endpoint detectors for speech signal processing

Accurate endpoint detection is a necessary capability for the construction of speech databases from field recordings. We describe the implementation of two endpoint detection algorithms which use signal features based on energy and rate of zero crossings. We have made extensive use of object-oriented concepts and data-driven programming to make our code re-usable for a variety of applications, including speech recognition. A uniform user-interface for both algorithms has been developed using a novel virtual class methodology. We also present a comparison of the two algorithms using an objective evaluation paradigm we have developed. A small locally prepared database has been used for the purpose of evaluation