# An analysis of large speech models-based representations for speech emotion recognition

arXiv: https://arxiv.org/abs/2311.00394 · HTML: https://mlsanity.com/paper/2311.00394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [On the Contribution of Lexical Features to Speech Emotion Recognition](https://mlsanity.com/paper/2509.05634/) (2025)

## Abstract (arXiv)

Large speech models-derived features have recently shown increased performance over signal-based features across multiple downstream tasks, even when the networks are not finetuned towards the target task. In this paper we show the results of an analysis of several signal- and neural models-derived features for speech emotion recognition. We use pretrained models and explore their inherent potential abstractions of emotions. Simple classification methods are used so as to not interfere or add knowledge to the task. We show that, even without finetuning, some of these large neural speech models' representations can enclose information that enables performances close to, and even beyond state-of-the-art results across six standard speech emotion recognition datasets.