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.

References

• TRANSCRIPT-FORMAT-EXAMPLES.md

• VOICE-PROFILE-TEMPLATE.md