A filter-based approach to detect end-of-utterances from prosody in dialog systems

We propose an efficient method to detect end-of-utterances from prosodic information in conversational speech. Our method is based on the application of a large set of binary and ramp filters to the energy and fundamental frequency signals obtained from the speech signal. These filter responses, which can be computed very efficiently, are used as input to a learning algorithm that generates the final detector. Preliminary experiments using data obtained from conversations show that an accurate classifier can be trained efficiently and that good results can be obtained without requirin