14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

Paralinguistic Event Detection from Speech Using Probabilistic Time-Series Smoothing and Masking

Rahul Gupta, Kartik Audhkhasi, Sungbok Lee, Shrikanth Narayanan

University of Southern California, USA

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.

Full Paper

Bibliographic reference.  Gupta, Rahul / Audhkhasi, Kartik / Lee, Sungbok / Narayanan, Shrikanth (2013): "Paralinguistic event detection from speech using probabilistic time-series smoothing and masking", In INTERSPEECH-2013, 173-177.