Active learning by sparse instance tracking and classifier confidence in acoustic emotion recognition

Data scarcity is an ever crucial problem in the field of acoustic emotion recognition. How to get the most informative data from a huge amount of data by least human work and at the same time to obtain the highest performance is quite important. In this paper, we propose and investigate two active learning strategies in acoustic emotion recognition: Based on sparse instances or based on classifier confidence scores. The first strategy focuses on the problem of unbalanced binary or multiple classes. The latter strategy pays more attention on clearing up the boundary confusion between different classes. Our experimental results show that by using active learning aiming at sparse instances or based on classifier confidence, the amount of transcribed data needed is significantly reduced and the unweigted accuracy boosts greatly as well.