Evolutionary Feature Generation in Speech Emotion Recognition

Abstract

Feature sets are broadly discussed within speech emotion recognition by acoustic analysis. While popular filter and wrapper based search help to retrieve relevant ones, we feel that automatic generation of such allows for more flexibility throughout search. The basis is formed by dynamic low-level descriptors considering intonation, intensity, formants, spectral information and others. Next, systematic derivation of prosodic, articulatory, and voice quality high level functionals is performed by descriptive statistical analysis. From here on feature alterations are automatically fulfilled, to find an optimal representation within feature space in view of a target classifier. To avoid NP-hard exhaustive search, we suggest use of evolutionary programming. Significant overall performance improvement over former works can be reported on two public databases

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