Accelerating manual annotation of filled pauses by automatic pre-selection

One objective of affective computing is the automatic processing of human emotions. Considering human speech, filled pauses are one of the cues giving insight into the emotional state of a human being. Filled pauses are short speech events without a specified semantic meaning, but they have a variety of communicative and affective functions. The detection and processing of such speech events can help a technical system to recognise the affective state of the user. To solve this task using machine learning methods, huge amounts of annotated data and thus human resources are necessary. In this paper we introduce an efficient approach for semiautomatic labelling of filled pauses aiming at finding as many of them as possible with minimal effort. We investigate to which extent such an approach can reduce the effort of manual transcription of filled pauses. By using our approach, we could for the first time quantify that the time necessary for the human supervised verification can be reduced by up to 85% compared to a full manual annotation.