Enterprise AI Analysis
Affective computing has changed: the foundation model disruption
The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of Al-based tools by the general public. We even observe an incursion of these models into disciplines related to human psychology, such as the Affective Computing domain, suggesting their affective, emerging capabilities. In this work, we aim to raise awareness of the power of Foundation Models in the field of Affective Computing by synthetically generating and analysing multimodal affective data, focusing on vision, linguistics, and speech (acoustics). We also raise awareness of evaluation problems related to the use of Foundation Models in this research area.
Authors: Björn Schuller, Adria Mallol-Ragolta, Alejandro Peña Almansa, Iosif Tsangko, Mostafa M. Amin, Anastasia Semertzidou, Lukas Christ, Shahin Amiriparian | Publication Date: 31 January 2026
Executive Impact: Key Metrics
Our analysis reveals the following critical performance indicators:
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ViT-FER ACCURACY on AffectNet (Vision)
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RoBERTa UAR on GoEmotions (Linguistics)
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FM Performance Gap vs. Fine-tuned (UAR)
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Total Vision Training Samples (AffectNet)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Vision Modality: The Impact of Generative AI
Foundation Models like Stable Diffusion XL are revolutionizing synthetic affective image generation. We explore their capabilities in creating photorealistic, cartoon, anime, and 3D emotional faces, and assess their affective quality using advanced FER models.
Linguistic Modality: LLMs for Affective Text Generation and Analysis
Large Language Models (LLMs) like LLaMA2, Mistral, and Mixtral are capable of affective style transfer and zero-shot emotion recognition from text. We evaluate their ability to generate emotionally nuanced phrases and classify emotions in unseen data.
Speech Modality: Emerging Frontiers and Current Limitations
While generative AI has significantly advanced Text-to-Speech (TTS), affective speech synthesis and zero-shot emotion recognition in speech by Foundation Models are still in nascent stages. We examine the current landscape and future potential.
Enterprise Process Flow
Expert-crafted Features
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Traditional ML (SVM)
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Deep Learning (DNN) with Data-driven Representations
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Neural Architecture Search (NAS)
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Foundation Models (FM) with Emergent Capabilities
57.5%
Highest Accuracy (ACC) for 3D style generated images (ViT-FER)
| Model | Input | ACC (%) |
|---|---|---|
| LLaMA2 7B |
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| LLaMA2 7B |
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| LLaVA1.5 7B |
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| ViT-FER |
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Linguistic Affective Style Transfer: Neutral to Surprise
Foundation Models demonstrate a remarkable ability to transform neutral text into emotively charged phrases, showcasing their understanding of emotional nuances.
Challenge: Transform the neutral phrase 'The weather is clear and sunny.' into a phrase conveying 'surprise' using different LLMs while maintaining semantic coherence.
Solution: Utilize Mixtral, Mistral, and LLaMA models with specific prompts to generate surprised versions of the input sentence, demonstrating varying but effective stylistic changes.
Result: Mixtral: 'Wow! What a surprise! The sky is astonishingly bright and clear today!' Mistral: 'The sudden emergence of unobstructed sunlight has taken me by complete astonishment!' LLaMA: 'It comes as quite a shock to discover that the sky has transformed itself into such crystal clarity!'
~15.81%
Average UAR gap between fine-tuned RoBERTa and zero-shot GPT-4 on GoEmotions
| Model | UAR (%) | ACC (%) |
|---|---|---|
| LLaMA2-7B |
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| Mistral |
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| GPT-4 |
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| ROBERTa (Fine-tuned) |
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Still Emerging
Affective Speech Synthesis & Zero-Shot Analysis Status
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