# A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

arXiv: https://arxiv.org/abs/2303.08027 · HTML: https://mlsanity.com/paper/2303.08027/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [MultiBench: Multiscale Benchmarks for Multimodal Representation Learning](https://mlsanity.com/paper/2107.07502/) (2021)
- [A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding](https://mlsanity.com/paper/2111.02735/) (2021)
- [Attentive Pooling Networks](https://mlsanity.com/paper/1602.03609/) (2016)
- [The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts](https://mlsanity.com/paper/2205.01780/) (2022)
- [Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion](https://mlsanity.com/paper/2210.15842/) (2022)
- [The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression](https://mlsanity.com/paper/2207.03572/) (2022)
- [Redundancy Reduction Twins Network: A Training framework for Multi-output Emotion Regression](https://mlsanity.com/paper/2206.09142/) (2022)
- [Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations](https://mlsanity.com/paper/2206.10695/) (2022)
- [Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track](https://mlsanity.com/paper/2206.11968/) (2022)
- [Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Bursts](https://mlsanity.com/paper/2206.12469/) (2022)
- [Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression](https://mlsanity.com/paper/2206.11049/) (2022)

## Built on by (most cited first, 1 of 1)

- [The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data](https://mlsanity.com/paper/2403.14048/) (2024)

## Abstract (arXiv)

As 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.