In this contribution, we introduce a novel approach to combine acoustic information and emotional point information for a robust automatic recognition of a speaker's emotion. Six discrete emotional states are recognized in the work. Firstly, a multi-level model for emotion recognition by acoustic features is presented. The derived features are selected by fisher rate to distinguish different types of emotions. Secondly, a novel emotional point model for Mandarin is established by Support Vector Machine and Hidden Markov Model. This model contains 28 emotional syllables which reflect rich emotional information. Finally the acoustic information and emotional point information are integrated by a soft decision strategy. Experimental results show that the application of emotional point information in speech emotion recognition is effective.
ASJC Scopus subject areas
- Artificial Intelligence