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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Non-verbal communication extends beyond gestures and facial expressions to include vocal elements known as paralanguage. Paralanguage consists of non-verbal vocal cues such as pitch, loudness, speech rate, pauses, and non-verbal vocalizations like laughter, sighs, and moans. These elements not only accompany speech but also provide critical emotional and contextual information.The Role of Paralanguage in CommunicationParalanguage adds depth to spoken language by conveying emotions and...
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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Quantum AI in Speech Emotion Recognition.

Michael Norval1, Zenghui Wang1

  • 1Department of Electrical and Smart Systems Engineering, University of South Africa, Florida, Johannesburg 1709, South Africa.

Entropy (Basel, Switzerland)
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study benchmarks hybrid quantum-classical models for speech emotion recognition (SER) using Afrikaans speech data. Quantum approaches showed lower accuracy than classical baselines, highlighting current quantum computing limitations.

Keywords:
AfrikaansMFCCQAOAQSVMnoise mitigationquantum machine learningspeech emotion recognitionvariational quantum classifier

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Area of Science:

  • Quantum Computing
  • Speech Emotion Recognition
  • Machine Learning

Background:

  • Speech Emotion Recognition (SER) is crucial for human-computer interaction.
  • Hybrid quantum-classical approaches offer potential for complex pattern recognition tasks.
  • Evaluating quantum algorithms on real-world data, like Afrikaans speech, is essential for assessing their practical viability.

Purpose of the Study:

  • To evaluate a hybrid quantum-classical pipeline for SER on an Afrikaans corpus.
  • To compare the performance of three quantum classifiers (VQC, QSVM, QAOA) against a CNN-LSTM baseline.
  • To establish an end-to-end, noise-aware benchmark for hybrid SER systems.

Main Methods:

  • Utilized Mel-frequency cepstral coefficients (MFCCs) with pitch and energy variants for spectral features.
  • Implemented classical-to-quantum data encoding using angle embedding and feature-to-qubit mapping.
  • Compared variational quantum classifier (VQC), quantum support vector machine (QSVM), and QAOA-based classifier against a CNN-LSTM baseline.

Main Results:

  • Quantum models achieved 41-43% test accuracy in ideal simulations.
  • Under realistic 1% NISQ noise, quantum model accuracy degraded to 34-40%.
  • The CNN-LSTM baseline achieved 73.9% test accuracy, significantly outperforming quantum approaches.

Conclusions:

  • Current NISQ-era quantum models exhibit lower empirical accuracy for SER compared to classical baselines.
  • The study provides a valuable noise-aware benchmark for hybrid SER systems.
  • Asymptotic advantages of quantum subroutines are noted for future fault-tolerant quantum computing regimes.