# Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?

arXiv: https://arxiv.org/abs/2402.01579 · HTML: https://mlsanity.com/paper/2402.01579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [ML-SUPERB: Multilingual Speech Universal PERformance Benchmark](https://mlsanity.com/paper/2305.10615/) (2023)
- [Speech Emotion Recognition using Self-Supervised Features](https://mlsanity.com/paper/2202.03896/) (2022)
- [Universal Paralinguistic Speech Representations Using Self-Supervised Conformers](https://mlsanity.com/paper/2110.04621/) (2021)
- [x-vectors meet emotions: A study on dependencies between emotion and speaker recognition](https://mlsanity.com/paper/2002.05039/) (2020)
- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [SERAB: A multi-lingual benchmark for speech emotion recognition](https://mlsanity.com/paper/2110.03414/) (2021)
- [TRILLsson: Distilled Universal Paralinguistic Speech Representations](https://mlsanity.com/paper/2203.00236/) (2022)
- [Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks](https://mlsanity.com/paper/2305.18640/) (2023)
- [Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion Recognition](https://mlsanity.com/paper/2308.08713/) (2023)
- [Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data](https://mlsanity.com/paper/2306.17199/) (2023)

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

- [PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition](https://mlsanity.com/paper/2506.01138/) (2025)

## Abstract (arXiv)

Availability of representations from pre-trained models (PTMs) have facilitated substantial progress in speech emotion recognition (SER). Particularly, representations from PTM trained for paralinguistic speech processing have shown state-of-the-art (SOTA) performance for SER. However, such paralinguistic PTM representations haven't been evaluated for SER in linguistic environments other than English. Also, paralinguistic PTM representations haven't been investigated in benchmarks such as SUPERB, EMO-SUPERB, ML-SUPERB for SER. This makes it difficult to access the efficacy of paralinguistic PTM representations for SER in multiple languages. To fill this gap, we perform a comprehensive comparative study of five SOTA PTM representations. Our results shows that paralinguistic PTM (TRILLsson) representations performs the best and this performance can be attributed to its effectiveness in capturing pitch, tone and other speech characteristics more effectively than other PTM representations.