Detecting Laughter and Filler Events by Time Series Smoothing with Genetic Algorithms

摘要

Social signal detection, where the aim is to identify vocalizations like laughter and filler events (sounds like "eh", "er", etc.) is a popular task in the area of computational paralinguistics, a subfield of speech technology. Recent studies have shown that besides applying state-of-the-art machine learning methods, it is worth making use of the contextual information and adjusting the frame-level scores based on the local neighbourhood. In this study we apply a weighted average time series smoothing filter for laughter and filler event identification, and set the weights using genetic algorithms. Our results indicate that this is a viable way of improving the Area Under the Curve (AUC) scores: our resulting scores are much better than the accuracy of the raw likelihoods produced by both AdaBoost.MH and DNN, and we also significantly outperform standard time series filters as well.