Laughter Classification Using Deep Rectifier Neural Networks with a Minimal Feature Subset

Laughter is one of the most important paralinguistic events, and it has specific roles in human conversation. The automatic detection of laughter occurrences in human speech can aid automatic speech recognition systems as well as some paralinguistic tasks such as emotion detection. In this study we apply Deep Neural Networks (DNN) for laughter detection, as this technology is nowadays considered state-of-the-art in similar tasks like phoneme identification. We carry out our experiments using two corpora containing spontaneous speech in two languages (Hungarian and English). Also, as we find it reasonable that not all frequency regions are required for efficient laughter detection, we will perform feature selection to find the sufficient feature subset.

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Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.