# Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2501.12050 · HTML: https://mlsanity.com/paper/2501.12050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 65 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics](https://mlsanity.com/paper/2103.09148/) (2021)
- [CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation](https://mlsanity.com/paper/1802.05630/) (2018)
- [Direct Modelling of Speech Emotion from Raw Speech](https://mlsanity.com/paper/1904.03833/) (2019)
- [The role of entanglement for enhancing the efficiency of quantum kernels towards classification](https://mlsanity.com/paper/2209.05142/) (2022)
- [An Ensemble 1D-CNN-LSTM-GRU Model with Data Augmentation for Speech Emotion Recognition](https://mlsanity.com/paper/2112.05666/) (2021)
- [Transition Role of Entangled Data in Quantum Machine Learning](https://mlsanity.com/paper/2306.03481/) (2023)
- [Multimodal Emotion Recognition from Raw Audio with Sinc-convolution](https://mlsanity.com/paper/2402.11954/) (2024)

## Abstract (arXiv)

Quantum machine learning (QML) offers a promising avenue for advancing representation learning in complex signal domains. In this study, we investigate the use of parameterised quantum circuits (PQCs) for speech emotion recognition (SER) a challenging task due to the subtle temporal variations and overlapping affective states in vocal signals. We propose a hybrid quantum classical architecture that integrates PQCs into a conventional convolutional neural network (CNN), leveraging quantum properties such as superposition and entanglement to enrich emotional feature representations. Experimental evaluations on three benchmark datasets IEMOCAP, RECOLA, and MSP-IMPROV demonstrate that our hybrid model achieves improved classification performance relative to a purely classical CNN baseline, with over 50% reduction in trainable parameters. This work provides early evidence of the potential for QML to enhance emotion recognition and lays the foundation for future quantum-enabled affective computing systems.