# CopyPaste: An Augmentation Method for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2010.14602 · HTML: https://mlsanity.com/paper/2010.14602/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech](https://mlsanity.com/paper/2208.05445/) (2022)
- [Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors](https://mlsanity.com/paper/2203.04443/) (2022)
- [Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning](https://mlsanity.com/paper/2202.02611/) (2022)
- [EdgeYOLO: An Edge-Real-Time Object Detector](https://mlsanity.com/paper/2302.07483/) (2023)
- [Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP](https://mlsanity.com/paper/2304.00860/) (2023)
- [Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer](https://mlsanity.com/paper/2211.08843/) (2022)
- [Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems](https://mlsanity.com/paper/2104.08806/) (2021)
- [Beyond Isolated Utterances: Conversational Emotion Recognition](https://mlsanity.com/paper/2109.06112/) (2021)
- [A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition](https://mlsanity.com/paper/2211.05047/) (2022)

## Abstract (arXiv)

Data augmentation is a widely used strategy for training robust machine learning models. It partially alleviates the problem of limited data for tasks like speech emotion recognition (SER), where collecting data is expensive and challenging. This study proposes CopyPaste, a perceptually motivated novel augmentation procedure for SER. Assuming that the presence of emotions other than neutral dictates a speaker's overall perceived emotion in a recording, concatenation of an emotional (emotion E) and a neutral utterance can still be labeled with emotion E. We hypothesize that SER performance can be improved using these concatenated utterances in model training. To verify this, three CopyPaste schemes are tested on two deep learning models: one trained independently and another using transfer learning from an x-vector model, a speaker recognition model. We observed that all three CopyPaste schemes improve SER performance on all the three datasets considered: MSP-Podcast, Crema-D, and IEMOCAP. Additionally, CopyPaste performs better than noise augmentation and, using them together improves the SER performance further. Our experiments on noisy test sets suggested that CopyPaste is effective even in noisy test conditions.