| name | voice-extractor |
| description | Extract voice patterns from transcripts - speaking style, phrases, vocabulary for authentic author profiles |
Voice Extractor
Extract authentic voice characteristics from transcripts to enhance author profiles.
Purpose
Transform transcript data (podcasts, interviews, meetings, videos) into actionable writing guidelines that capture an author's authentic voice, making AI-generated content sound natural and personal.
When to Use
- Author has recordings/transcripts of themselves speaking
- Want to capture authentic speaking patterns
- Need to enhance a manually-created author profile
- Building a new author profile from scratch using transcripts
- Refining an existing profile with more data
Workflow
1. Prepare Transcripts
Accept transcripts in these formats:
- Plain text:
Speaker: text
- Timestamped:
[00:01:23] Speaker: text or 59:54 Speaker: text
- Bracketed:
[Speaker]: text
- WhatsApp:
[17:30, 12/6/2025] Speaker: text
- SRT subtitles: Standard subtitle format
If user provides audio/video without transcript:
Suggest transcription services:
- YouTube auto-captions (downloadable)
- Otter.ai, Descript
- OpenAI Whisper (local)
- Rev.com
2. Run Analysis
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --list-speakers transcript.txt
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --speaker "Name" transcript.txt
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --speaker "Name" t1.txt t2.txt t3.txt
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --speaker "Name" --json transcript.txt
3. Enhance Author Profile
The extracted data enhances these author fields:
| Extracted Data | Maps To |
|---|
| Communication style | tone.formality, tone.opinionated |
| Characteristic expressions | phrases.signature |
| Sentence starters | phrases.signature |
| Signature vocabulary | vocabulary.use_freely |
| Speaking style | notes |
4. Manual Enhancement
After automated extraction, read transcript samples to identify:
- Unique phrases: Catchphrases missed by frequency analysis
- Humor style: Sarcasm, self-deprecation, wit patterns
- Story structure: How they set up anecdotes
- Cultural markers: Regional expressions, analogies
- Topic emphasis: What makes them animated
What Gets Extracted
Sentence Structure
- Average sentence length
- Variety (short/moderate/long/complex)
- Question frequency
Communication Style
- Enthusiasm (love, amazing, awesome)
- Hedging (maybe, perhaps, I think)
- Certainty (definitely, absolutely)
- Empathy (understand, appreciate)
- Directness (need to, must, bottom line)
- Storytelling (so, and then, eventually)
- Analytical (because, therefore, however)
Characteristic Expressions
- Fillers: "you know", "I mean", "like", "right"
- Hedgers: "kind of", "sort of", "I guess"
- Emphatics: "honestly", "literally", "definitely"
Vocabulary
- Signature words used frequently
- Vocabulary richness percentage
Integration with Author Profile
New Author from Transcript
/article-writer:author analyze --speaker "John" transcripts/*.txt
/article-writer:author add
Enhance Existing Author
/article-writer:author analyze --speaker "John" --author-id mwguerra new_podcast.txt
Output Format
JSON Output (for merging)
{
"voice_analysis": {
"extracted_from": ["podcast_ep1.txt", "interview.txt"],
"sample_count": 156,
"total_words": 12450,
"sentence_structure": {
"avg_length": 14.5,
"variety": "moderate length, conversational",
"question_ratio": 12.3
},
"communication_style": [
{ "trait": "enthusiasm", "percentage": 28.5 },
{ "trait": "analytical", "percentage": 24.1 },
{ "trait": "directness", "percentage": 18.7 }
],
"characteristic_expressions": [
"you know",
"I think",
"the thing is",
"at the end of the day"
],
"sentence_starters": [
"I think",
"So the",
"And then",
"But the"
],
"signature_vocabulary": [
"actually",
"basically",
"approach",
"strategy",
"implementation"
],
"analyzed_at": "2025-01-15T10:00:00Z"
},
"suggested_updates": {
"tone": {
"formality": 5,
"opinionated": 7
},
"phrases": {
"signature": ["you know", "the thing is", "at the end of the day"]
},
"vocabulary": {
"use_freely": ["approach", "strategy", "implementation"]
}
}
}
Markdown Report
# Voice Analysis: John Smith
*Analyzed 156 speaking turns, 12,450 words*
## Speaking Style
- **Sentence length**: Moderate (~14 words avg)
- **Questions**: Uses questions occasionally (12%)
- **Vocabulary richness**: 45% unique words
## Communication Style
- **Primary**: Enthusiastic (28%)
- **Secondary**: Analytical (24%)
- **Tertiary**: Direct (19%)
## Characteristic Expressions
- "you know" (used 45x)
- "I think" (used 38x)
- "the thing is" (used 22x)
## Sentence Starters
- "I think..." (28x)
- "So the..." (19x)
- "And then..." (15x)
## Signature Vocabulary
**actually** (67x), **basically** (45x), **approach** (34x)
---
## Recommendations for Author Profile
Based on this analysis:
- Set formality to 5 (conversational but professional)
- Set opinionated to 7 (confident, uses "I think" but states opinions)
- Add signature phrases: "you know", "the thing is"
- Use vocabulary freely: approach, strategy, implementation
Quality Indicators
Good voice analysis needs:
- 100+ speaking turns for reliable patterns
- 5,000+ words for vocabulary analysis
- Multiple contexts (different topics/conversations)
Low data warning:
⚠️ Limited data: Only 23 speaking turns found.
Results may not fully represent speaking patterns.
Consider adding more transcripts.
References
