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Human Laughter Generation using Hybrid Generative Models
KSII Transactions on Internet and Information Systems
Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysisdoi:10.3837/tiis.2021.05.001 fatcat:7735kpjyyjbydllji4wsai5nry