A personalized emotion recognition system using an unsupervised feature adaptation scheme

A personalized emotion recognition system aims to tune the model to recognize the expressive behaviors of a targeted person. Such a system can play an important role in various domains including call center and health care applications. Adapting any general emotion recognition system for a particular individual requires speech samples and prior knowledge about their emotional content. These assumptions constrain the use of these techniques in many real scenarios in which no annotated data is available to train or adapt the models. To address this problem, this paper introduces an unsupervised feature adaptation scheme that aims to reduce the mismatch between the acoustic features used to train the system and the acoustic features extracted from the unknown targeted speaker. The adaptation scheme uses our recently proposed iterative feature normalization (IFN) framework. An emotion detection system is trained with the IEMOCAP database. For testing, a database was created by downloading videos from a video-sharing website, containing various interviews from a targeted subject (1.5 hours). The detection system is used to identify emotional speech with and without the proposed feature adaptation scheme. The experimental results indicate that the proposed approach improves the unweighted accuracy from 50.8% to 70.0%.