{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/ai-for-speech-emotion-recognition",
  "headline": "AI for Speech Emotion Recognition: Vocal Biomarkers, Mental Health Screening, and Affective Computing",
  "description": "Your voice carries rich information about your emotional state. AI systems can now analyze speech patterns -- pitch, rhythm, tone, pauses -- to detect depression, anxiety, and stress with clinical-grade accuracy, enabling passive, scalable mental health screening through everyday voice interactions.",
  "dateCreated": "2026-05-24T02:49:13.538Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Improving speech depression detection using transfer learning with wav2vec 2.0 in low-resource environments",
      "sameAs": "https://www.nature.com/articles/s41598-024-60278-1"
    },
    {
      "@type": "CreativeWork",
      "name": "Speech-based Clinical Depression Screening: An Empirical Study",
      "sameAs": "https://arxiv.org/abs/2406.03510"
    }
  ]
}