# EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2203.13617 · HTML: https://mlsanity.com/paper/2203.13617/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search](https://mlsanity.com/paper/1907.05737/) (2019)
- [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://mlsanity.com/paper/1904.12760/) (2019)
- [NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing](https://mlsanity.com/paper/2006.07116/) (2020)
- [Speech Emotion Recognition via Contrastive Loss under Siamese Networks](https://mlsanity.com/paper/1910.11174/) (2019)
- [Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition](https://mlsanity.com/paper/1708.07050/) (2017)

## Built on by (most cited first, 2 of 2)

- [Enhancing Speech Emotion Recognition Through Differentiable Architecture Search](https://mlsanity.com/paper/2305.14402/) (2023)
- [emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition](https://mlsanity.com/paper/2403.14083/) (2024)

## Abstract (arXiv)

Speech emotion recognition (SER) is an important research topic in human-computer interaction. Existing works mainly rely on human expertise to design models. Despite their success, different datasets often require distinct structures and hyperparameters. Searching for an optimal model for each dataset is time-consuming and labor-intensive. To address this problem, we propose a two-stream neural architecture search (NAS) based framework, called enquoteEmotionNAS. Specifically, we take two-stream features (i.e., handcrafted and deep features) as the inputs, followed by NAS to search for the optimal structure for each stream. Furthermore, we incorporate complementary information in different streams through an efficient information supplement module. Experimental results demonstrate that our method outperforms existing manually-designed and NAS-based models, setting the new state-of-the-art record.