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

# List speakers in transcript
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --list-speakers transcript.txt

# Extract for specific speaker
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --speaker "Name" transcript.txt

# Multiple transcripts (more data = better profile)
bun run "${CLAUDE_PLUGIN_ROOT}"/scripts/voice-extractor.ts --speaker "Name" t1.txt t2.txt t3.txt

# Output JSON for direct use with author profiles
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

# 1. Extract voice data
/article-writer:author analyze --speaker "John" transcripts/*.txt

# 2. Review and confirm extraction
# Claude will show extracted patterns

# 3. Add identity info
/article-writer:author add
# Answer: name, role, expertise, languages

# 4. Merge voice data
# Claude will combine extracted + manual data

Enhance Existing Author

# 1. Extract from new transcripts
/article-writer:author analyze --speaker "John" --author-id mwguerra new_podcast.txt

# 2. Review changes
# Claude shows what will be updated

# 3. Confirm merge
# Voice analysis data added to existing profile

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.

mwguerra-voice-extractor