---
id: ai-for-speech-emotion-recognition
title: >-
  AI for Speech Emotion Recognition: Vocal Biomarkers, Mental Health Screening, and Affective
  Computing
schema_type: article
category: ai
language: en
confidence: medium
last_verified: '2026-05-28'
created_date: '2026-05-24'
generation_method: ai_structured
ai_models:
  - claude-4.5-sonnet
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
completeness: 0.85
atomic_facts:
  - id: fact-ai-001
    statement: RAVDESS is a validated multimodal database of emotional speech and song recordings.
    source_title: The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
    source_url: https://doi.org/10.1371/journal.pone.0196391
    confidence: medium
  - id: fact-ai-002
    statement: >-
      IEMOCAP is an interactive emotional dyadic motion capture database used in emotion recognition
      research.
    source_title: 'IEMOCAP: Interactive emotional dyadic motion capture database'
    source_url: https://sail.usc.edu/iemocap/
    confidence: medium
  - id: fact-ai-003
    statement: >-
      The Speech Communication review surveys emotional models, databases, features, preprocessing
      methods, modalities, and classifiers for speech emotion recognition.
    source_title: >-
      Speech emotion recognition: Emotional models, databases, features, preprocessing methods,
      supporting modalities, and classifiers
    source_url: https://doi.org/10.1016/j.specom.2018.01.006
    confidence: medium
primary_sources:
  - title: The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
    type: academic_paper
    year: 2018
    url: https://doi.org/10.1371/journal.pone.0196391
    institution: PLOS ONE
  - title: 'IEMOCAP: Interactive emotional dyadic motion capture database'
    type: dataset_paper
    year: 2008
    url: https://sail.usc.edu/iemocap/
    institution: USC SAIL
  - title: >-
      Speech emotion recognition: Emotional models, databases, features, preprocessing methods,
      supporting modalities, and classifiers
    type: academic_paper
    year: 2018
    url: https://doi.org/10.1016/j.specom.2018.01.006
    institution: Speech Communication
known_gaps:
  - >-
    Coverage intentionally narrowed to directly sourced public evidence; adjacent subtopics are not
    exhaustively covered.
disputed_statements: []
secondary_sources: []
updated: '2026-05-28'
---
## 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)