mlsanityData to 7 Oct 2026

Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation

arXiv 2102.01813 ↗First posted 3 Feb 2021cs.SD cs.LG eess.AScited by 12

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Abstract (arXiv)

In Speech Emotion Recognition (SER), emotional characteristics often appear in diverse forms of energy patterns in spectrograms. Typical attention neural network classifiers of SER are usually optimized on a fixed attention granularity. In this paper, we apply multiscale area attention in a deep convolutional neural network to attend emotional characteristics with varied granularities and therefore the classifier can benefit from an ensemble of attentions with different scales. To deal with data sparsity, we conduct data augmentation with vocal tract length perturbation (VTLP) to improve the generalization capability of the classifier. Experiments are carried out on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. We achieved 79.34% weighted accuracy (WA) and 77.54% unweighted accuracy (UA), which, to the best of our knowledge, is the state of the art on this dataset.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 5 of its 24 references that are in the map. First posted on arXiv 3 Feb 2021.

Builds on

5 of 24 references.

References · 5

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  3. 2020Attention Driven Fusion for Multi-Modal Emotion Recognitioncited by 11
  4. 2019Semi-Supervised Speech Emotion Recognition with Ladder Networkscited by 9
  5. 2018Area Attentioncited by 5

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 5; 2023: 2; 2024: 1; 2025: 3.

12 papers.

  1. 2022Dawn of the transformer era in speech emotion recognition: closing the valence gapcited by 155
  2. 2022Learning Speech Emotion Representations in the Quaternion Domaincited by 7
  3. 2023Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAPcited by 6
  4. 2022A New Amharic Speech Emotion Dataset and Classification Benchmarkcited by 5
  5. 2022Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representationcited by 4
  6. 2022Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learningcited by 2
  7. 2021Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognitioncited by 1
  8. 2023Speech and Text-Based Emotion Recognizercited by 0
  9. 2024Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognitioncited by 0
  10. 2025Less Stress, More Privacy: Stress Detection on Anonymized Speech of Air Traffic Controllerscited by 0
  11. 2025Transformer Redesign for Late Fusion of Audio-Text Features on Ultra-Low-Power Edge Hardwarecited by 0
  12. 2025Multi-Loss Learning for Speech Emotion Recognition with Energy-Adaptive Mixup and Frame-Level Attentioncited by 0