Detection of laughter in children's speech using spectral and prosodic acoustic features

Laughter is an important para-linguistic cue that can be useful in gauging the affective state of the speaker. In this paper, we present an approach to detecting laughter in children’s speech using acoustic features in the spectral and prosodic domains. Feature selection was performed using the information gain-based technique and a speaker-independent validation using a support vector machine (SVM), an accuracy of 94.43% was observed, which was a 12.48% absolute improvement over the baseline result of 81.95%. For us to explore generalization properties, the models of speech and laughter were tested on a completely different database of adult-child interactions known as the Multimodal Dyadic Behavior Dataset (MDBD). The accuracy using the earlier trained models was 70.58%. Even though the children in this database were toddlers (less than three years old), the results suggest that the predictive power of the selected features generalizes well to different forms of children’s laughter.