# Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network

arXiv: https://arxiv.org/abs/2109.04316 · HTML: https://mlsanity.com/paper/2109.04316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)](https://mlsanity.com/paper/1903.12094/) (2019)
- [Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition](https://mlsanity.com/paper/1708.07050/) (2017)
- [The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild](https://mlsanity.com/paper/1806.10658/) (2018)
- [Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training](https://mlsanity.com/paper/2010.11226/) (2020)

## Abstract (arXiv)

In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models. Previously, neural network designs, such as Multitask Learning, have accounted for variations in emotional expressions due to demographic and contextual factors. However, existing models face a few constraints: 1) they rely on a clear definition of domains (e.g. gender, noise condition, etc.) and the availability of domain labels; 2) they often attempt to learn domain-invariant features while emotion expressions can be domain-specific. In the present study, we propose the Nonparametric Hierarchical Neural Network (NHNN), a lightweight hierarchical neural network model based on Bayesian nonparametric clustering. In comparison to Multitask Learning approaches, the proposed model does not require domain/task labels. In our experiments, the NHNN models generally outperform the models with similar levels of complexity and state-of-the-art models in within-corpus and cross-corpus tests. Through clustering analysis, we show that the NHNN models are able to learn group-specific features and bridge the performance gap between groups.