A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition
Refereed conference paper presented and published in conference proceedings

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AbstractAs a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal bursts are indispensable for developing robust and general artificial intelligence. Exploring computational approaches for understanding vocal bursts is attracting increasing research attention. In this work, we propose a hierarchical framework, based on chain regression models, for affective recognition from VBs, that explicitly considers multiple relationships: (i) between emotional states and diverse cultures; (ii) between low-dimensional (arousal & valence) and high-dimensional (10 emotion classes) emotion spaces; and (iii) between various emotion classes within the high-dimensional space. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACII Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO" and "CULTURE" tasks. Experimental results based on the ACII Challenge 2022 dataset demonstrate the superior performance of the proposed system and the effectiveness of considering multiple relationships using hierarchical regression chain models.
Acceptance Date08/06/2023
All Author(s) ListJinchao Li, Xixin Wu, Kaitao Song, Dongsheng Li, Xunying Liu, Helen Meng
Name of ConferenceIEEE ICASSP2023
Start Date of Conference04/06/2023
End Date of Conference10/06/2023
Place of ConferenceRhodes Island
Country/Region of ConferenceGreece
Proceedings TitleICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing
Year2023
ISSN1520-6149
LanguagesEnglish-United States

Last updated on 2024-16-09 at 12:50