# Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks

arXiv: https://arxiv.org/abs/2305.18640 · HTML: https://mlsanity.com/paper/2305.18640/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2023.

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

- [Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition](https://mlsanity.com/paper/2307.10757/) (2023)
- [Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?](https://mlsanity.com/paper/2402.01579/) (2024)
- [Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models](https://mlsanity.com/paper/2409.14131/) (2024)
- [Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?](https://mlsanity.com/paper/2506.02258/) (2025)

## Abstract (arXiv)

Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields. A current trend in methods used for SER is to leverage embeddings from pre-trained models (PTMs) as input features to downstream models. However, the use of embeddings from speaker recognition PTMs hasn't garnered much focus in comparison to other PTM embeddings. To fill this gap and in order to understand the efficacy of speaker recognition PTM embeddings, we perform a comparative analysis of five PTM embeddings. Among all, x-vector embeddings performed the best possibly due to its training for speaker recognition leading to capturing various components of speech such as tone, pitch, etc. Our modeling approach which utilizes x-vector embeddings and mel-frequency cepstral coefficients (MFCC) as input features is the most lightweight approach while achieving comparable accuracy to previous state-of-the-art (SOTA) methods in the CREMA-D benchmark.