# Reusing Neural Speech Representations for Auditory Emotion Recognition

arXiv: https://arxiv.org/abs/1803.11508 · HTML: https://mlsanity.com/paper/1803.11508/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition](https://mlsanity.com/paper/2201.11826/) (2022)
- [Disentangling Prosody Representations with Unsupervised Speech Reconstruction](https://mlsanity.com/paper/2212.06972/) (2022)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [Foundations of Multisensory Artificial Intelligence](https://mlsanity.com/paper/2404.18976/) (2024)

## Abstract (arXiv)

Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcity of training data, general subjectivity in emotion perception resulting in low annotator agreement, and the uncertainty about which features are the most relevant and robust ones for classification. In this paper, we will tackle the latter problem. Inspired by the recent success of transfer learning methods we propose a set of architectures which utilize neural representations inferred by training on large speech databases for the acoustic emotion recognition task. Our experiments on the IEMOCAP dataset show ~10% relative improvements in the accuracy and F1-score over the baseline recurrent neural network which is trained end-to-end for emotion recognition.