Abstract
Current machine-based techniques for vocal emotion recognition only consider a finite number of clearly labeled emotional classes whereas the kinds of emotional classes and their number are typically application dependent. Previous studies have shown that multistage classification scheme, because of ambiguous nature of affect classes, helps to improve emotion classification accuracy. However, these multistage classification schemes were manually elaborated by taking into account the underlying emotional classes to be discriminated. In this paper, we propose an automatically elaborated hierarchical classification scheme (ACS), which is driven by an evidence theory-based embedded feature-selection scheme (ESFS), for the purpose of application-dependent emotions' recognition. Experimented on the Berlin dataset with 68 features and six emotion states, this automatically elaborated hierarchical classifier (ACS) showed its effectiveness, displaying a 71.38% classification accuracy rate compared to a 71.52% classification rate achieved by our previously dimensional model-driven but still manually elaborated multistage classifier (DEC). Using the DES dataset with five emotion states, our ACS achieved a 76.74% recognition rate compared to a 81.22% accuracy rate displayed by a manually elaborated multistage classification scheme (DEC).
Highlights
Speech emotion analysis has attracted growing interest within the context of increasing awareness of the wide application potential of affective computing [1, 2]
Experimented on the Berlin dataset with 68 features and six emotion states, this automatically elaborated hierarchical classifier (ACS) showed its effectiveness, displaying a 71.38% classification accuracy rate compared to a 71.52% classification rate achieved by our previously dimensional model-driven but still manually elaborated multistage classifier (DEC)
Experimented on the Berlin dataset with 68 features and six emotion states, this automatically elaborated hierarchical classifier (ACS) showed its effectiveness, displaying a 71.38% accuracy rate compared to a 71.52% classification rate achieved by our previously dimensional model-driven but still manually elaborated multistage classifier (DEC)
Summary
Speech emotion analysis has attracted growing interest within the context of increasing awareness of the wide application potential of affective computing [1, 2]. Current machine-based techniques for vocal emotion recognition only consider classification problems of a finite number of discrete emotion categories [3] whereas the kinds of emotional states and their number are typically application dependent. These affective categories can be the six basic emotional states and some nonbasic emotional classes, including for instance deception [4], certainty [5], stress [6], confidence, confusion, and frustration [7]. The evidence theory was completed and presented by Shafer in [26] It relies on the definition of a set of n hypothesis Ω which have to be exclusive and exhaustive. The elementary mass function or belief mass which presents the chance of being a true statement is defined as m : 2Ω −→ [0, 1],
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