# Training speech emotion classifier without categorical annotations

arXiv: https://arxiv.org/abs/2210.07642 · HTML: https://mlsanity.com/paper/2210.07642/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding](https://mlsanity.com/paper/2111.02735/) (2021)
- [x-vectors meet emotions: A study on dependencies between emotion and speaker recognition](https://mlsanity.com/paper/2002.05039/) (2020)
- [Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels](https://mlsanity.com/paper/1805.10339/) (2018)
- [Emotion Recognition From Speech With Recurrent Neural Networks](https://mlsanity.com/paper/1701.08071/) (2017)
- [Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions](https://mlsanity.com/paper/2002.01323/) (2020)

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

- [A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions](https://mlsanity.com/paper/2401.00536/) (2023)

## Abstract (arXiv)

There are two paradigms of emotion representation, categorical labeling and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. The proposed approach contains a regressor model which is trained to predict a vector of continuous values in dimensional representation for given speech audio. The output of this model can be interpreted as an emotional category using a mapping algorithm. We investigated the performances of a combination of three feature extractors, three neural network architectures, and three mapping algorithms on two different corpora. Our study shows the advantages and limitations of the classification via regression approach.