# AI for Speech Emotion Recognition: Vocal Biomarkers, Mental Health Screening, and Affective Computing
Status: public
Confidence: medium (0.78) (verified)
Last verified: 2026-05-28
Generation: ai_structured

## TL;DR

Speech emotion recognition uses acoustic and sometimes linguistic features to classify affective states from speech. This repair removes clinical-grade and benchmark-number claims and lowers confidence to medium.

## Core Explanation

The evidence-focused article treats SER as an affective-computing task supported by benchmark datasets and surveys. It avoids claiming clinical validity for mental-health screening unless a specific clinical validation source is in scope.

## Further Reading

- [The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)](https://doi.org/10.1371/journal.pone.0196391)
- [IEMOCAP: Interactive emotional dyadic motion capture database](https://sail.usc.edu/iemocap/)
- [Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers](https://doi.org/10.1016/j.specom.2018.01.006)

## Related Articles

- [Affective Computing: Multimodal Emotion Recognition, Sentiment Analysis, and Empathetic AI](../affective-computing.md)
- [AI for Audio Processing: Speech Recognition, Music Generation, and Sound Understanding](../ai-for-audio-processing-speech-recognition-music-generation-and-sound-understanding.md)
- [AI for Mental Health: LLM-Based Therapy, Digital Interventions, and Clinical Trials](../ai-for-mental-health.md)