Finding emotionally involved speech using implicitly proximity-annotated laughter

Browsing through collections of audio recordings of conversation nominally relies on the processing of participants' lexical productions. The evolving verbal and non-verbal context of those productions, likely indicative of the degree of participant involvement, is often ignored. The present work explores the relevance of laughter to the retrieval of conversation intervals in which the speech of one or more participants is prosodically or pragmatically marked as involved. Experiments indicate that the relevance of laughter depends on its temporal distance to the laugher's speech. The results suggest that in order to be pertinent to downstream emotion recognition applications, laughter detection systems must first and foremost detect that laughter which is most temporally proximate to the laugher's speech.

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