Method for detecting emotions involving subspace specialists
First Claim
1. Method for detecting emotions from speech input, wherein at least the first feature class (A) and a second feature class (E) of features are at least in part evaluated, derived and/or extracted from a given speech input (SI), wherein from said features a current emotional state (CES) of the current speaker and/or parameters (CFS) thereof are derived and wherein said first and second feature classes (A, E) are identified and/or associated with, in particular distinct, dimensions of an underlying emotional manifold (EM) or emotional space (ES) and/or with subspaces thereof, in particular with activation or arousal and evaluation or pleasure, respectively.
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Abstract
To detect and determine a current emotional state (CES) of a human being from a spoken speech input (SI), it is suggested in a method for detecting emotions to identify first and second feature classes (A, E) with, in particular distinct, dimensions of an underlying emotional manifold (EM) or emotional space (ES) and/or with subspaces thereof.
67 Citations
15 Claims
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1. Method for detecting emotions from speech input,
wherein at least the first feature class (A) and a second feature class (E) of features are at least in part evaluated, derived and/or extracted from a given speech input (SI), wherein from said features a current emotional state (CES) of the current speaker and/or parameters (CFS) thereof are derived and wherein said first and second feature classes (A, E) are identified and/or associated with, in particular distinct, dimensions of an underlying emotional manifold (EM) or emotional space (ES) and/or with subspaces thereof, in particular with activation or arousal and evaluation or pleasure, respectively.
- 10. Method according to claim 10, wherein said classifier system (CE) of high complexity includes a multiplicity of classifiers, in particular by implementing speaker dependencies, such as age, gender and/or the like.
Specification