8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


Emotion Recognition Using a Data-Driven Fuzzy Inference System

Chul Min Lee, Shrikanth Narayanan

University of Southern California, USA

The need and importance of automatically recognizing emotions from human speech has grown with the increasing role of human-computer interaction applications. This paper explores the detection of domain-specific emotions using a fuzzy inference system to detect two emotion categories, negative and nonnegative emotions. The input features are a combination of segmental and suprasegmental acoustic information; feature sets are selected from a 21-dimensional feature set and applied to the fuzzy classifier. Our fuzzy inference system is designed through a data-driven approach. The design of the fuzzy inference system has two phases: one for initialization for which fuzzy c-means method is used, and the other is fine-tuning of parameters of the fuzzy model. For fine-tuning, a well known neuro-fuzzy method are used. Results from on spoken dialog data from a call center application show that the optimized FIS with two rules (FIS-2) improves emotion classification by 63.0% for male data and 73.7% for female over previous results obtained using linear discriminant classifier.

Full Paper

Bibliographic reference.  Lee, Chul Min / Narayanan, Shrikanth (2003): "Emotion recognition using a data-driven fuzzy inference system", In EUROSPEECH-2003, 157-160.