Laughter detection using ALISP-based N-Gram models

Laughter is a very complex behavior that communicates a wide range of messages with different meanings. It is highly dependent on social and interpersonal attributes. Most of the previous works (e.g. [1, 2]) on automatic laughter detection from audio uses frame-level acoustic features as parameters to train their machine learning techniques, such as Gaussian Mixture Models (GMMs), Support Vector Machines (SVMs) etc. However, segmental approaches that capture higher-level events have not been adequately focussed due to the nonlinguistic nature of laughter. This paper is an attempt to detect laughter regions with the help of automatically acquired acoustic segments using Automatic Language Independent Speech Processing (ALISP) [3, 4] models.