Athens Emotional States Inventory (AESI)
AESI is a new dataset in Greek containing audio recordings of five categorical emotions: anger, fear, joy, sadness and neutral. The items of the AESI consist of 35 sentences each having content indicative of the corresponding emotion. The resulting data include recordings from 20 participants (12 male, 8 female), which resulted in 696 utterances with a total duration of 27 mins, 51 sec.
Abstract: The development of ecologically valid procedures for collecting reliable and unbiased emotional data towards computer interfaces with social and affective intelligence targeting patients with mental disorders. Following its development, presented with, the Athens Emotional States Inventory (AESI) proposes the design, recording and validation of an audiovisual database for five emotional states: anger, fear, joy, sadness and neutral. The items of the AESI consist of sentences each having content indicative of the corresponding emotion. Emotional content was assessed through a survey of 40 young participants with a questionnaire following the Latin square design. The emotional sentences that were correctly identified by 85% of the participants were recorded in a soundproof room with microphones and cameras. A preliminary validation of AESI is performed through automatic emotion recognition experiments from speech. The resulting database contains 696 recorded utterances in Greek language by 20 native speakers and has a total duration of approximately 28 min. Preliminary results yielded an accuracy of 75.15% for automatically classifying the emotions in AESI, indicating the usefulness of the proposed approach for collecting emotional data with reliable content, balanced across classes, and with reduced environmental variability.
The total size of the dataset is 295MB. Click here to download the dataset.
Reference to:
Chaspari, T., Soldatos, C., & Maragos, P. (2015). The development of the Athens Emotional States Inventory (AESI): collection, validation and automatic processing of emotionally loaded sentences. The World Journal of Biological Psychiatry, 16(5), 312-322.
Communication:
For more information or details regarding the dataset contact:
Theodora Chaspari, PhD
Assistant Professor
HUman Bio-Behavioral Signals (HUBBS) Lab
Texas A&M University
URL: https://chaspari.engr.tamu.edu
Email: chaspari@tamu.edu
