Automatic laughter detection using neural networks

Laughter recognition is an underexplored area of research. Our goal in this work was to develop an accurate and efficient method to recognize laughter segments, ultimately for the purpose of speaker recognition. Previous work has classified presegmented data as to the presence of laughter using SVMs, GMMs, and HMMs. In this work, we have extended the stateof-the-art in laughter recognition by eliminating the need to presegment the data, while attaining high precision, as well as yielding higher resolution for labeling start and end times. In our experiments, we found neural networks to be a particularly good fit for this problem and the score level combination of the MFCC, AC PEAK, and F0 features to be optimal. We achieved an equal error rate (EER) of 7.9% for laughter recognition, thereby establishing the first results for nonpresegmented frame-by-frame laughter recognition on the ICSI Meetings database.