Voice Blend

voice-blend

Combine multiple voice profiles with weighted mixing to create hybrid voices.

Triggers

Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

• "70/30 [voice A]/[voice B]" → ratio-based voice blending
• "blend voices" → voice mixing

Behavior

When triggered, this skill:

1. Loads source voice profiles from:

   • Built-in templates (voices/templates/)
   • Project voices (.aiwg/voices/)
   • User voices (~/.config/aiwg/voices/)

2. Parses blend specification:

   • Equal blend: "blend X and Y" → 50%/50%
   • Weighted blend: "70% X, 30% Y" → explicit weights
   • Multi-voice: "blend X, Y, and Z" → equal thirds

3. Interpolates dimensions:

   • Weighted average of tone values (formality, confidence, etc.)
   • Merged vocabulary lists (union of prefer, intersection of avoid)
   • Dominant structure patterns from highest-weighted voice

4. Generates hybrid profile with clear lineage tracking

Usage Examples

Equal Blend

User: "Blend technical-authority and friendly-explainer"

Result: 50/50 blend
- formality: 0.45 (avg of 0.7 and 0.2)
- confidence: 0.8 (avg of 0.9 and 0.7)
- warmth: 0.55 (avg of 0.3 and 0.8)
- vocabulary: merged from both

Weighted Blend

User: "80% executive-brief, 20% casual-conversational"

Result: Weighted blend
- formality: 0.7 (0.8*0.85 + 0.2*0.15)
- confidence: 0.86 (0.8*0.9 + 0.2*0.6)
- Dominant structure from executive-brief

Multi-Voice Blend

User: "Combine technical-authority, friendly-explainer, and executive-brief"

Result: Equal thirds (33.3% each)
- All dimensions averaged across three profiles
- Vocabulary merged from all three

Blend Algorithm

Dimension Interpolation

For each tone dimension:

blended_value = Σ(weight_i × value_i) / Σ(weight_i)

Vocabulary Merging

• prefer: Union of all prefer lists, deduplicated
• avoid: Intersection of all avoid lists (only avoid if ALL sources avoid)
• signature_phrases: Top N from each source (weighted by blend ratio)

Structure Resolution

Structure settings use the dominant voice (highest weight):

• sentence_length: from dominant
• paragraph_length: from dominant
• use_lists: from dominant
• use_examples: averaged (rarely=0, when-appropriate=1, frequently=2)

Perspective Handling

• person: Majority vote (tie goes to second person)
• voice: Active unless all sources use passive
• tense: Present unless all sources use past

Output Format

name: technical-friendly-blend
version: 1.0.0
description: Blended voice profile
blend_sources:
  - name: technical-authority
    weight: 0.7
  - name: friendly-explainer
    weight: 0.3
tone:
  formality: 0.55  # interpolated
  confidence: 0.84 # interpolated
  warmth: 0.45     # interpolated
  energy: 0.49     # interpolated
  complexity: 0.65 # interpolated
vocabulary:
  prefer:
    - precise technical terminology  # from technical
    - concrete examples              # from friendly
  avoid:
    - marketing superlatives         # common to both
  signature_phrases:
    - "The system handles..."        # from technical (70%)
    - "Think of it like..."          # from friendly (30%)
structure:
  sentence_length: medium            # from dominant (technical)
  use_examples: frequently           # averaged

Output Location

Blended profiles are saved to:

1. .aiwg/voices/{name}.yaml (default)
2. Custom path with --output flag
3. ~/.config/aiwg/voices/ with --global flag

Integration

• Input: Takes profiles created by voice-create or built-in templates
• Output: Creates profiles usable by voice-apply
• Chain: voice-analyze → voice-blend → voice-apply

CLI Usage

# Equal blend of two voices
python voice_blender.py --voices "technical-authority,friendly-explainer"

# Weighted blend
python voice_blender.py --voices "technical-authority:0.7,friendly-explainer:0.3"

# Custom output name
python voice_blender.py --voices "..." --name my-hybrid-voice

# Output to specific directory
python voice_blender.py --voices "..." --output .aiwg/voices/

# JSON output for inspection
python voice_blender.py --voices "..." --json

Error Handling

• Profile not found: Lists available profiles and suggests closest match
• Invalid weights: Normalizes weights to sum to 1.0
• Incompatible profiles: Warns but proceeds with best-effort blend

References

• Voice loader: ../voice-apply/scripts/voice_loader.py
• Schema: ../../../schemas/voice-profile.schema.json
• Built-in templates: ../../voices/templates/

Downstream writing handoff

Use author-controlled writing workflows for briefs, explicit profile selection, channel/revision APIs and receipts. Analysis or blending does not establish author identity, approve sample rights or activate a provider transformation. Keep legacy imports explicit and use separately accepted sidecar preferences for author-controlled execution.