Automated Classification of Children’s Linguistic versus Non-Linguistic Vocalisations
Zixing Zhang, Alejandrina Cristià, Anne S. Warlaumont, Björn Wolfgang Schuller · 2018
A key outstanding task for speech technology involves dealing with non-standard speakers, notably young children.Distinguishing children's linguistic from non-linguistic vocalisations is crucial for a number of applied and fundamental research goals, and yet there are few systems available for such a classification.This paper investigates two large-scale framelevel acoustic feature sets (eGeMAPS and ComParE16) followed by a dynamic model (GRU-RNN), and two kinds of derived static feature sets on the segment level (functional-based and Bag of Audio Words) combined with a static model (SVM), and automatically learnt representations directly from original raw voice signals by using an end-to-end system.These are applied to a large database of children's vocalisations (total N = 6,298) drawn from daylong recordings gathered in Namibia, Bolivia, and Vanuatu.Among these systems, the one implemented with GRU-RNN using ComParE16 features empirically performs best.We further identify promising paths of further research, including the application of a finer-grained classification of children's vocalisations onto these data, and the exploration of other feature systems.