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Non-verbal speech cues serve multiple functions in human interaction such as maintaining the conversational flow as well as expressing emotions, personality, and interpersonal attitude. In particular, non-verbal vocalizations such as laughters are associated with affective expressions while vocal fillers are used to hold the floor during a conversation. The Interspeech 2013 Social Signals Sub-Challenge involves detection of these two types of non-verbal signals in telephonic speech dialogs. We extend the challenge baseline system by using filtering and masking techniques on probabilistic time series representing the occurrence of a vocal event. We obtain improved area under receiver operating characteristic (ROC) curve of 93.3% (10.4% absolute improvement) for laughters and 89.7% (6.1% absolute improvement) for fillers on the test set. This improvement suggests the importance of using temporal context for detecting these paralinguistic events.