Analyze voice match between draft and client
AI compares ghostwritten content against client voice samples, scoring match quality and identifying specific deviations.
Analyze voice match between draft and client
River's Voice Match Analyzer compares ghostwritten drafts against authentic client voice samples, providing detailed analysis of how well you've captured their voice. You provide both your draft and client reference materials (interview transcripts, their published writing, or emails). The AI analyzes sentence structure patterns, vocabulary choices, tone consistency, and stylistic markers, then generates voice match score with specific feedback showing where voice aligns perfectly and where it deviates. This systematic comparison catches voice issues before clients do.
Unlike subjective impressions that voice 'feels right,' this tool quantifies voice match across specific dimensions: sentence length variance, vocabulary overlap, formality level, metaphor patterns, and emotional tone. It identifies subtle deviations you might miss—Client A uses contractions 80% of the time but your draft only 40%, or they average 18-word sentences while yours average 26. These measurable differences create voice mismatch clients notice even if they can't articulate what's wrong. The analysis pinpoints exactly what to adjust.
This tool is perfect for ghostwriters wanting objective voice verification before client submission, writers managing multiple clients who worry about voice cross-contamination, or anyone receiving vague client feedback that voice is 'off' but need specifics. If you wonder whether voice match is accurate or just hopeful thinking, or if you want to catch problems before clients do, systematic analysis provides that confidence. Use it after completing draft sections but before major client reviews, when objective feedback most valuable.
Why Voice Match Matters More Than Content Quality
You can write brilliantly and still fail if voice doesn't match. Ghostwriting's fundamental promise is making content sound like the credited author wrote it personally. When voice match fails, readers sense inauthenticity even without consciously identifying why. They think 'this person has ghostwriter' rather than 'this person is genuine authority.' For memoir especially, voice authenticity is everything—readers want to believe they're hearing true person's story in their actual voice, not writer imitating them. Voice mismatch breaks that illusion.
The challenge is voice assessment requires distance most ghostwriters don't have when deep in project. After writing 40,000 words channeling someone, your judgment about voice accuracy becomes unreliable. You think you hear their voice because you've been living in it for months. Objective analysis—whether from human editor or AI tool—provides necessary perspective. It measures voice match quantitatively across multiple dimensions rather than relying on subjective feel that may be distorted by familiarity.
Early voice verification prevents expensive revision cycles. If first draft has fundamental voice mismatch requiring rewriting rather than tweaking, better to discover that before showing client. Voice problems caught early (after 10,000 words) require limited rewriting and course correction. Voice problems caught late (after showing client 60,000-word manuscript) require massive revision damaging timeline and client confidence. Systematic voice checking at draft stage is insurance policy preventing catastrophic failure at submission stage.
What You Get
Voice match score (0-100%) showing overall alignment between draft and client voice
Dimension-by-dimension analysis: sentence structure, vocabulary, tone, formality, patterns
Specific examples where voice matches perfectly and where it deviates with explanations
Quantitative metrics comparing your draft to client patterns (sentence length, word choice frequency, etc.)
Prioritized improvement recommendations addressing most significant voice gaps first
Side-by-side comparison passages showing authentic client voice versus your draft for direct contrast
How It Works
- 1Provide draft and referenceSubmit your ghostwritten draft plus client voice samples (interview transcripts, their writing, style guide) for comparison
- 2AI analyzes voice matchSystem compares across multiple dimensions: structure, vocabulary, tone, patterns, generating detailed analysis (10-15 minutes)
- 3Review match score and feedbackSee overall voice match percentage plus specific areas of alignment and deviation with improvement guidance
- 4Revise based on analysisAdjust draft targeting identified voice gaps before showing client, ensuring authentic voice match
Frequently Asked Questions
What voice match score should I aim for?
85%+ is strong voice match indicating draft sounds authentically like client. 70-85% is acceptable but needs refinement—voice is mostly there with some noticeable deviations. Below 70% indicates significant voice mismatch requiring substantial revision. However, score is guide, not absolute. Sometimes intentional elevation (polishing their casual speech into more formal prose) creates lower scores even when result serves project. Use score as diagnostic triggering closer examination, not pass/fail threshold. If score is low but you believe voice serves client's goals, that's valid creative choice.
How much client reference material do I need?
Minimum 1,000 words of authentic client voice—ideally 2,000-5,000 words from multiple sources. More reference material produces more accurate analysis. Best sources: interview transcripts (their natural speech), emails or casual writing (authentic informal voice), published articles if available (their polished voice). Avoid using only highly edited professional writing if ghostwriting requires casual voice—you need samples matching the style you're attempting. If reference material is limited, analysis will be less precise but still valuable for identifying obvious mismatches.
Can this analyze voice for multiple clients to prevent cross-contamination?
Yes—excellent use case. Provide draft for Client A plus voice references for both Client A and Client B. Analysis will show if Client B's voice characteristics have contaminated Client A's manuscript. For example: 'Draft includes 15 instances of business jargon matching Client B patterns but foreign to Client A vocabulary' or 'Sentence length matches Client B average (26 words) rather than Client A average (16 words).' This catches cross-contamination you might miss when managing multiple simultaneous projects.
What if analysis says voice doesn't match but client loves the draft?
Client approval overrides analysis. Sometimes clients want more polished voice than they naturally speak, or they appreciate your framing of their ideas better than their own expression. If client says 'This is exactly what I wanted,' voice match mission accomplished regardless of score. However, if client hasn't seen draft yet, low score is warning to examine carefully before submission. Analysis catches problems before clients notice them. Use it as early warning system, not final arbiter.
Does this work for books or just shorter content?
Works for any length but analyze in manageable sections: chapters or 3,000-5,000 word chunks rather than entire 60,000-word manuscript. Analyzing full book at once produces overwhelming feedback difficult to process. Better approach: analyze chapter 1, refine based on feedback, analyze chapter 2, continue through manuscript. This catches voice drift early and lets you apply learnings to subsequent chapters before writing them. Voice can drift over long projects—regular checking prevents cumulative deviation.
Can I improve my voice match score through multiple revisions?
Yes—iterative refinement works. First analysis might show 68% match. Revise targeting identified gaps. Second analysis shows 78% match with remaining deviations highlighted. Third revision brings you to 87%. This iterative approach is more effective than trying to fix everything at once. Focus each revision pass on specific dimension: first pass fix sentence length, second pass adjust vocabulary, third pass refine tone. Trying to fix all dimensions simultaneously often produces worse results than systematic targeted revisions.
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