Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst Tasks

ICASSP 2023  ·  Bagus Tris Atmaja, Akira Sasou ·

The search for emotional biomarkers within the human voice is a challenging research area. Previous studies focused on predicting affective state from speech; this study explores various tasks on affective vocal bursts. Borrowing the success of self-supervised learning in automatic speech recognition, we extracted acoustic embedding using variants of wav2vec 2.0 for four affective vocal bursts tasks: High, Two, Culture, and Type. Using a similar architecture for all tasks, the evaluation of acoustic embeddings reveals the potential use of wav2vec 2.0 variants over the conventional acoustic features in affective vocal bursts tasks. We evaluated both conventional acoustic features and these acoustic embeddings on the different number of twenty seeds evaluation and reported the maximum and average scores with their standard deviations in the validation set. Three high scores from these validations for all tasks assist the generation of predictions for the test set. We compared the test scores with previous studies and obtained remarkable improvements.



Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Vocal Bursts Type Prediction HUME-VB w2v2-r-er Average Recall 0.4902 # 1
Cultural Vocal Bursts Intensity Prediction HUME-VB w2v2-r-er Concordance correlation coefficient (CCC) 0.5199 # 2
Vocal Bursts Valence Prediction HUME-VB w2v2-r-er Concordance correlation coefficient (CCC) 0.6290 # 1
Vocal Bursts Intensity Prediction HUME-VB w2v2-r-vad Concordance correlation coefficient (CCC) 0.6554 # 2