Yusuf Canbolat, David Rutkowski, Leslie Rutkowski
Global pattern in hunger and educational opportunity: a multilevel analysis of child hunger and TIMSS mathematics achievement
Large-scale assessments in education
emotion recognition models using audio input data enable development interactive systems applications mental healthcare marketing gaming social media analysis field affective computing using audio data rich major barrier achieve consistently highperformance models paucity available training labels selfsupervised learning ssl family methods learn despite scarcity supervised labels predicting properties data understand utility selfsupervised learning audiobased emotion recognition applied selfsupervised learning pretraining classification emotions cmu multimodal opinion sentiment emotion intensity cmu moseis acoustic data unlike prior papers experimented raw acoustic data technique applied encoded acoustic data 74 parameters distinctive audio features discrete timesteps model first pretrained uncover randomly masked timestamps acoustic data pretrained model finetuned using small sample annotated data performance final model evaluated via overall mean absolute error mae mean absolute error mae per emotion overall fourclass accuracy fourclass accuracy per emotion metrics compared baseline deep learning model identical backbone architecture find selfsupervised learning consistently improves performance model across metrics especially number annotated data points finetuning step small furthermore quantify behaviors selfsupervised model convergence amount annotated data increases work characterizes utility selfsupervised learning affective computing demonstrating selfsupervised learning useful number training examples small effect pronounced emotions easier classify happy sad angry work demonstrates selfsupervised learning still improves performance applied embedded feature representations rather traditional approach pretraining raw input space