Automatic detection of laughter
conference paper
In the context of detecting paralinguistic events with the aim to make classification of the speakers emotional state possible, a detector was developed for one of the most obvious paralinguistic events, namely laughter. Gaussian Mixture Models were trained with Perceptual Linear Prediction features, pitch&energy, pitch&voicing and modulation spectrum features to model laughter and speech. Data from the ICSI Meeting Corpus and the Dutch CGN corpus were used for our classification experiments. The results showed that Gaussian Mixture Models trained with Perceptual Linear Prediction features performed best with Equal Error Rates ranging from 7.1%-20.0%.
Topics
TNO Identifier
15958
Source
9th European Conference on Speech Communication and Technology, 4 September 2005 through 8 September 2005, Lisbon,, pp. 485-488.
Source title
Pages
485-488
Files
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