# Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine

arXiv: https://arxiv.org/abs/2105.09406 · HTML: https://mlsanity.com/paper/2105.09406/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 1 of 1)

- [Progressive Neural Networks for Transfer Learning in Emotion Recognition](https://mlsanity.com/paper/1706.03256/) (2017)

## Abstract (arXiv)

Herein, we have compared the performance of SVM and MLP in emotion recognition using speech and song channels of the RAVDESS dataset. We have undertaken a journey to extract various audio features, identify optimal scaling strategy and hyperparameter for our models. To increase sample size, we have performed audio data augmentation and addressed data imbalance using SMOTE. Our data indicate that optimised SVM outperforms MLP with an accuracy of 82 compared to 75%. Following data augmentation, the performance of both algorithms was identical at ~79%, however, overfitting was evident for the SVM. Our final exploration indicated that the performance of both SVM and MLP were similar in which both resulted in lower accuracy for the speech channel compared to the song channel. Our findings suggest that both SVM and MLP are powerful classifiers for emotion recognition in a vocal-dependent manner.