Name: Potato
Author: Potato

Emotionserkennung in Sprache (SER) steckt in Sprachassistenten, in Anwendungen für psychische Gesundheit und in der Auswertung von Callcentern, und alles davon braucht gelabeltes Audio zum Trainieren. Dieses Tutorial geht Annotationsoberflächen für kategoriale Emotionen, dimensionale Bewertungen und Clips mit mehr als einer Emotion durch. Die zugrunde liegenden Audio-Optionen stehen in der Dokumentation zur Audio-Annotation.

Ansätze für Emotionsannotation

Es gibt einige übliche Wege, Sprachemotion zu labeln. Du kannst diskrete Kategorien wie fröhlich, traurig oder wütend verwenden. Du kannst kontinuierliche Dimensionen wie Valenz, Erregung und Dominanz bewerten. Du kannst Annotierende mehrere Emotionen gleichzeitig mit Intensitätsbewertungen markieren lassen. Oder du taggst bei längeren Clips verschiedene Emotionen zu verschiedenen Zeitmarken.

Kategoriale Emotionsklassifikation

Grundeinrichtung

yaml

annotation_task_name: "Speech Emotion Recognition"
 
data_files:
  - data/utterances.json
 
item_properties:
  id_key: id
  audio_key: audio_path
  text_key: transcript  # Optional transcript
 
audio:
  enabled: true
  display: waveform
  waveform_color: "#8B5CF6"
  progress_color: "#A78BFA"
  speed_control: true
  speed_options: [0.75, 1.0, 1.25]
 
annotation_schemes:
  - annotation_type: radio
    name: emotion
    description: "What emotion is expressed in this speech?"
    labels:
      - name: Happy
        description: "Joy, excitement, amusement"
        keyboard_shortcut: "h"
      - name: Sad
        description: "Sorrow, disappointment, grief"
        keyboard_shortcut: "s"
      - name: Angry
        description: "Frustration, irritation, rage"
        keyboard_shortcut: "a"
      - name: Fearful
        description: "Anxiety, worry, terror"
        keyboard_shortcut: "f"
      - name: Surprised
        description: "Astonishment, shock"
        keyboard_shortcut: "u"
      - name: Disgusted
        description: "Revulsion, distaste"
        keyboard_shortcut: "d"
      - name: Neutral
        description: "No clear emotion"
        keyboard_shortcut: "n"
    label_requirement:
      required: true

Potato rendert neben den Annotationslabels eine interaktive Wellenform mit Wiedergabesteuerung:

Oberfläche zur Emotionsklassifikation auf Audio mit Wellenform und Emotionslabels Die Audio-Annotationsoberfläche mit interaktiver Wellenform, Wiedergabesteuerung und kategorialen Emotionslabels

Intensität ergänzen

yaml

annotation_schemes:
  - annotation_type: radio
    name: emotion
    labels: [Happy, Sad, Angry, Fearful, Surprised, Disgusted, Neutral]
    label_requirement:
      required: true
 
  - annotation_type: likert
    name: intensity
    description: "How intense is this emotion?"
    size: 5
    min_label: "Very weak"
    max_label: "Very strong"

Dimensionale Emotionsannotation

Das VAD-Modell (Valence-Arousal-Dominance) bewertet jeden Clip auf drei kontinuierlichen Skalen, statt ihn in eine Kategorie zu zwingen:

yaml

annotation_task_name: "Dimensional Emotion Rating"
 
annotation_schemes:
  # Valence: negative to positive
  - annotation_type: likert
    name: valence
    description: "Valence: How positive or negative?"
    size: 7
    min_label: "Very negative"
    max_label: "Very positive"
 
  # Arousal: calm to excited
  - annotation_type: likert
    name: arousal
    description: "Arousal: How calm or excited?"
    size: 7
    min_label: "Very calm"
    max_label: "Very excited"
 
  # Dominance: submissive to dominant
  - annotation_type: likert
    name: dominance
    description: "Dominance: How submissive or dominant?"
    size: 7
    min_label: "Very submissive"
    max_label: "Very dominant"

Bildskalen (SAM)

Im Stil des Self-Assessment Manikin:

yaml

annotation_schemes:
  - annotation_type: likert
    min_label: "Very negative"
    max_label: "Very positive"
    name: valence
    description: "Select the figure that matches the emotional valence"
    size: 9

Gemischte Emotionen erkennen

Für Sprache mit mehreren Emotionen:

yaml

annotation_schemes:
  - annotation_type: multiselect
    name: emotions_present
    description: "Select ALL emotions you detect (can be multiple)"
    labels:
      - Happy
      - Sad
      - Angry
      - Fearful
      - Surprised
      - Disgusted
      - Contempt
 
  - annotation_type: radio
    name: primary_emotion
    description: "Which emotion is MOST prominent?"
    labels:
      - Happy
      - Sad
      - Angry
      - Fearful
      - Surprised
      - Disgusted
      - Contempt
      - Mixed (no dominant)

Vollständige Emotionsannotation

yaml

annotation_task_name: "Comprehensive Speech Emotion Annotation"
 
data_files:
  - data/speech_samples.json
 
item_properties:
  id_key: id
  audio_key: audio_url
  text_key: transcript
 
audio:
  enabled: true
  display: waveform
  waveform_color: "#EC4899"
  progress_color: "#F472B6"
  height: 120
  speed_control: true
  speed_options: [0.5, 0.75, 1.0, 1.25]
  show_duration: true
  autoplay: false
 
# Show transcript if available
display:
  show_text: true
  text_field: transcript
  text_label: "Transcript (for reference)"
 
annotation_schemes:
  # Primary categorical emotion
  - annotation_type: radio
    name: primary_emotion
    description: "Primary emotion expressed"
    labels:
      - name: Happiness
        color: "#FCD34D"
        keyboard_shortcut: "1"
      - name: Sadness
        color: "#60A5FA"
        keyboard_shortcut: "2"
      - name: Anger
        color: "#F87171"
        keyboard_shortcut: "3"
      - name: Fear
        color: "#A78BFA"
        keyboard_shortcut: "4"
      - name: Surprise
        color: "#34D399"
        keyboard_shortcut: "5"
      - name: Disgust
        color: "#FB923C"
        keyboard_shortcut: "6"
      - name: Neutral
        color: "#9CA3AF"
        keyboard_shortcut: "7"
    label_requirement:
      required: true
 
  # Emotional intensity
  - annotation_type: likert
    name: intensity
    description: "Emotional intensity"
    size: 5
    min_label: "Very mild"
    max_label: "Very intense"
    label_requirement:
      required: true
 
  # Dimensional ratings
  - annotation_type: likert
    name: valence
    description: "Valence (negative to positive)"
    size: 7
    min_label: "Negative"
    max_label: "Positive"
 
  - annotation_type: likert
    name: arousal
    description: "Arousal (calm to excited)"
    size: 7
    min_label: "Calm"
    max_label: "Excited"
 
  # Voice quality
  - annotation_type: multiselect
    name: voice_qualities
    description: "Voice characteristics (select all that apply)"
    labels:
      - Trembling voice
      - Raised pitch
      - Lowered pitch
      - Loud/shouting
      - Soft/whisper
      - Fast speech rate
      - Slow speech rate
      - Breathy
      - Tense/strained
      - Crying
      - Laughing
 
  # Genuineness
  - annotation_type: radio
    name: authenticity
    description: "Does the emotion seem genuine?"
    labels:
      - Clearly genuine
      - Likely genuine
      - Uncertain
      - Likely acted/fake
      - Clearly acted/fake
 
  # Confidence
  - annotation_type: likert
    name: confidence
    description: "How confident are you in your annotation?"
    size: 5
    min_label: "Guessing"
    max_label: "Certain"
 
annotation_guidelines:
  title: "Emotion Annotation Guidelines"
  content: |
    ## Listening Instructions
    1. Listen to the entire clip before annotating
    2. You may replay as many times as needed
    3. Focus on the VOICE, not just the words
 
    ## Emotion Categories
    - **Happiness**: Joy, amusement, contentment
    - **Sadness**: Sorrow, disappointment, melancholy
    - **Anger**: Frustration, irritation, rage
    - **Fear**: Anxiety, nervousness, terror
    - **Surprise**: Astonishment, startle
    - **Disgust**: Revulsion, contempt
    - **Neutral**: Calm, matter-of-fact
 
    ## Tips
    - Consider tone, pitch, speaking rate
    - The transcript may not match the emotion
    - When unsure between two emotions, choose the stronger one
    - Use the intensity scale for unclear cases
 
output_annotation_dir: annotations/
export_annotation_format: jsonl

Ausgabeformat

json

{
  "id": "utt_001",
  "audio_url": "/audio/sample_001.wav",
  "transcript": "I can't believe this happened!",
  "annotations": {
    "primary_emotion": "Surprise",
    "intensity": 4,
    "valence": 2,
    "arousal": 6,
    "voice_qualities": ["Raised pitch", "Fast speech rate"],
    "authenticity": "Clearly genuine",
    "confidence": 4
  },
  "annotator": "rater_01",
  "timestamp": "2024-12-05T10:30:00Z"
}

Emotion auf Segmentebene

Für längeres Audio mit wechselnden Emotionen:

yaml

annotation_schemes:
  - annotation_type: audio_annotation
    name: emotion_segments
    description: "Mark time segments with different emotions"
    labels:
      - name: Happy
        color: "#FCD34D"
      - name: Sad
        color: "#60A5FA"
      - name: Angry
        color: "#F87171"
      - name: Neutral
        color: "#9CA3AF"

Qualitätskontrolle

yaml

quality_control:
  attention_checks:
    enabled: true
    gold_items:
      - audio: "/audio/gold/clearly_happy.wav"
        expected:
          primary_emotion: "Happiness"
          intensity: [4, 5]  # Accept 4 or 5
      - audio: "/audio/gold/clearly_angry.wav"
        expected:
          primary_emotion: "Anger"

Hinweise zur Emotionsannotation

Hör den ganzen Clip an, bevor du dich entscheidest, und achte darauf, wie etwas gesagt wird, statt auf die Wörter selbst. Denk daran, dass Ausdrucksnormen sich zwischen Kulturen unterscheiden: Was in der einen nach Wut klingt, kann in der anderen einfach Nachdruck sein. Emotionen zu annotieren ermüdet, ermuntere also zu Pausen und lass das Team regelmäßig über Uneinigkeiten sprechen, damit die Kalibrierung hält.

Nächste Schritte

Ergänze Sprecherdiarisierung, um Emotion über mehrere Sprecher zu verfolgen
Richte Crowdsourcing für Erhebungen in großem Umfang ein
Berechne die Inter-Annotator-Übereinstimmung für Emotionsaufgaben

Dokumentation unter Audio-Annotation.