Automatic laughter detection using neural networks
Mary Tai Knox, Nikki Mirghafori · 2007
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 pur-pose of speaker recognition. Previous work has classified pre-segmented data as to the presence of laughter using SVMs, GMMs, and HMMs. In this work, we have extended the state-of-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 par-ticularly good fit for this problem and the score level combi-nation of the MFCC, AC PEAK, and F0 features to be opti-mal. We achieved an equal error rate (EER) of 7.9 % for laugh-ter recognition, thereby establishing the first results for non-presegmented frame-by-frame laughter recognition on the ICSI Meetings database. Index Terms: laughter recognition, neural networks, speech in