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The human voice effectively communicates a range of emotions with nuancedvariations in acoustics. Existing emotional speech corpora are limited in thatthey are either (a) highly curated to induce specific emotions with predefinedcategories that may not capture the full extent of emotional experiences, or(b) entangled in their semantic and prosodic cues, limiting the ability tostudy these cues separately. To overcome this challenge, we propose a newapproach called 'Genetic Algorithm with People' (GAP), which integrates humandecision and production into a genetic algorithm. In our design, we allowcreators and raters to jointly optimize the emotional prosody over generations.We demonstrate that GAP can efficiently sample from the emotional speech spaceand capture a broad range of emotions, and show comparable results tostate-of-the-art emotional speech corpora. GAP is language-independent andsupports large crowd-sourcing, thus can support future large-scalecross-cultural research.

Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody