Active Learning for Speech Emotion Recognition Using Conditional Random Fields

With the increasing demand for spoken language interfaces in human-computer interactions, automatic recognition of emotional states from human speeches has become of increasing importance. Unfortunately, obtaining human annotations of emotion corpus to train a supervised system can become a laborious and costly effort. To address this, we explore active learning techniques with the objective of reducing the amount of human-annotated data needed to attain a given level of performance. In this paper we proposed an approach for speech emotion recognition based on Active Conditional Random Fields. Experiments show that for most of the cases considered, active selection strategies when recognizing speech emotion are as good as or exceed the performance of random data selection.