mlsanityData to 7 Oct 2026

Neural Architecture Search for Speech Emotion Recognition

arXiv 2203.16928 ↗First posted 31 Mar 2022cs.SD cs.CL cs.LGcited by 6

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Abstract (arXiv)

Deep neural networks have brought significant advancements to speech emotion recognition (SER). However, the architecture design in SER is mainly based on expert knowledge and empirical (trial-and-error) evaluations, which is time-consuming and resource intensive. In this paper, we propose to apply neural architecture search (NAS) techniques to automatically configure the SER models. To accelerate the candidate architecture optimization, we propose a uniform path dropout strategy to encourage all candidate architecture operations to be equally optimized. Experimental results of two different neural structures on IEMOCAP show that NAS can improve SER performance (54.89% to 56.28%) while maintaining model parameter sizes. The proposed dropout strategy also shows superiority over the previous approaches.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 31 Mar 2022.

Builds on

19 of 35 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  3. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  4. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  5. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  6. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  7. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  8. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 411
  9. 2017Large-Scale Evolution of Image Classifierscited by 348
  10. 2017Attentional Pooling for Action Recognitioncited by 54
  11. 2019Efficient Neural Architecture Search via Proximal Iterationscited by 31
  12. 2021LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Searchcited by 27
7 more
  1. 2019Speech Emotion Recognition with Dual-Sequence LSTM Architecturecited by 23
  2. 2020DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptationcited by 15
  3. 2022Neural Architecture Search For LF-MMI Trained Time Delay Neural Networkscited by 11
  4. 2021Darts-Conformer: Towards Efficient Gradient-Based Neural Architecture Search For End-to-End ASRcited by 10
  5. 2020Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradientscited by 7
  6. 2021Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Searchcited by 4
  7. 2020Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognitioncited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 1.

6 papers.

  1. 2022Resource-Efficient Separation Transformercited by 11
  2. 2022EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Searchcited by 11
  3. 2023Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive Mixupcited by 5
  4. 2023Frame-level emotional state alignment method for speech emotion recognitioncited by 2
  5. 2023Enhancing Speech Emotion Recognition Through Differentiable Architecture Searchcited by 1
  6. 2024emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognitioncited by 0