Abstract

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.

Date

2013

Authors

Rahul Gupta, Kartik Audhkhasi, Sungbok Lee, Shrikanth S Narayanan

Conference

Interspeech

Pages

173-177