Online detection of vocal Listener Responses with maximum latency constraints

When human listeners utter Listener Responses (e.g. back-channels or acknowledgments) such as ´yeah´ and ´mmhmm´, interlocutors commonly continue to speak or resume their speech even before the listener has finished his/her response. This type of speech interactivity results in frequent speech overlap which is common in human human conversation. To allow for this type of speech interactivity to occur between humans and spoken dialog systems, which will result in more human-like continuous and smoother human-machine inter action, we propose an on-line classifier which can classify incoming speech as Listener Responses. We show that it is possible to detect vocal Listener Responses using maximum latency thresholds of 100-500 ms, thereby obtaining equal error rates ranging from 34% to 28% by using an energy based voice activity detector.