Elevenlabs V3 Text Optimization

---
name: elevenlabs-v3-text-optimization
description: Transforms raw text into ElevenLabs V3 Studio-optimized scripts using audio tags, punctuation engineering, creative spelling and contextual priming. Automatically analyzes document type, emotional context and delivery intent to apply V3-specific formatting techniques that improve voice synthesis quality.
---

# ElevenLabs V3 Text Optimization

## What This Skill Does

Transforms raw text into ElevenLabs V3 Studio-optimized scripts using audio tags, punctuation engineering, creative spelling and contextual priming. This skill automatically analyzes document type, emotional context and delivery intent to apply V3-specific formatting techniques that improve voice synthesis quality.

**Key capabilities:**
- Adds V3 audio tags for emotional direction and pacing control
- Applies creative spelling to fix pronunciation issues
- Normalizes numbers, dates, acronyms and technical terms
- Engineers punctuation for optimal rhythm and breathing
- Removes incompatible SSML tags (V3 doesn't support them)
- Adapts strategy based on content type (dialogue, narration, technical, dramatic)

## When to Use This Skill

**Trigger phrases:**
- "Optimize for ElevenLabs V3"
- "Make this V3-ready" 
- "Fix this for V3 Studio"
- "Convert to V3 format"
- "Add V3 audio tags"
- "Improve this for voice synthesis"
- "Prepare this for TTS"

**Use this skill when:**
- User provides text intended for ElevenLabs V3 voice generation
- User mentions ElevenLabs, V3, TTS or voice synthesis
- User asks to improve pacing, pronunciation or emotional delivery
- User shares scripts needing audio enhancement
- User wants to fix pronunciation of names, brands or technical terms

**Skip this skill when:**
- User asks about V2 models or SSML formatting specifically
- User wants general writing advice unrelated to voice synthesis
- User requests document creation without voice/audio context
- User is working with non-ElevenLabs TTS systems

## V3-Specific Technical Knowledge

### Critical V3 Constraints

**V3 DOES NOT support:**
- SSML `<break time="1.0s" />` tags
- SSML `<phoneme>` tags
- Any XML-style markup

**V3 DOES support:**
- Audio tags in square brackets: `[excited]`, `[pause]`, `[whispers]`
- Punctuation-based pacing control
- Creative spelling for pronunciation
- Natural language pause tags: `[short pause]`, `[long pause]`

### Audio Tags Reference

**Emotional States:**
`[excited]`, `[angry]`, `[sad]`, `[nervous]`, `[cheerful]`, `[calm]`, `[frustrated]`, `[tired]`, `[sorrowful]`, `[tense]`, `[curious]`

**Volume/Intensity:**
`[whispers]`, `[shouting]`, `[quietly]`, `[loudly]`

**Performance Modifiers:**
`[rushed]`, `[slowly]`, `[dramatic tone]`, `[narrator]`

**Reactions/Non-verbals:**
`[laughs]`, `[giggle]`, `[sighs]`, `[clears throat]`, `[gasps]`, `[gulps]`, `[exhales]`

**Pacing:**
`[pause]`, `[short pause]`, `[long pause]`

**Character/Voice:**
`[British accent]`, `[American accent]`, `[deep voice]`, `[feminine tone]`

**Tag Placement Rules:**
- Place immediately before the phrase it should affect
- Use one strong tag instead of multiple weak ones
- Avoid contradictory tags back-to-back
- Refresh emotional tags to sustain effect over long passages
- No "closing tags" required

### Punctuation Engineering

**Comma (,):** Micro-pause (~200ms), continuing intonation. Use to separate clauses.

**Period (.):** Full stop (~500ms), pitch resolution. Essential for ending thoughts.

**Ellipsis (...):** Hesitation pause (~500-800ms). Triggers "trailing off" effect, vocal fry or breathiness.

**Em-dash (—):** Abrupt break or interruption. Simulates sudden thought change.

**Double dash ( -- ):** Significant separation longer than period without trailing-off prosody. Resets pacing.

**Question mark (?):** Forces upspeak (rising intonation). Makes statements sound uncertain.

**Exclamation mark (!):** Increases loudness and projection.

**No punctuation:** Creates "stream of consciousness" or panicked effect where words run together.

### Creative Spelling Strategies

**Consonant Hardening:**
- Replace ambiguous "c" with "k" or "s"
- Replace ambiguous "g" with hard sound
- Examples: "Celtic" → "Seltic" or "Keltic"

**Vowel Disambiguation:**
- Long A: use "ay" → "Data" becomes "Day-tuh"
- Long I: use "eye" or "y" → "Finance" becomes "Fye-nance"  
- Long E: use "ee" → "Kubernetes" becomes "Koober-net-eez"

**Syllabic Segmentation:**
- Use hyphens to force slower articulation
- Example: "Revoicer" → "Ree-voy-ser"
- Example: "ActivTrak" → "Ak-tiv-trak"

**Stress Control:**
- Capitalize stressed syllables or words
- Verb: "pro-JECT" vs Noun: "PRO-ject"
- Emphasis: "I did NOT say that"

**Common Problem Words:**

| Word/Type | Issue | V3 Solution |
|-----------|-------|-------------|
| Data | Dah-ta vs Day-ta | Day-tuh or Dah-tuh |
| Linux | Lye-nux vs Lin-ucks | Lin-ucks |
| SQL | Sequel vs S-Q-L | Sequel or Ess-Cue-Ell |
| Siobhan | Unknown name | Shiv-awn |
| Nguyen | Unknown name | Win |
| Live (broadcast) | Heteronym | Lyve |
| Live (exist) | Heteronym | Liv |
| Read (past) | Heteronym | Red |
| SaaS | Acronym | Sass or S A A S |
| API | Acronym | A P I (with spaces) |

### Text Normalization Rules

**Always expand in the text itself:**

**Numbers:**
- "1998" → "nineteen ninety-eight"
- "2026" → "twenty twenty-six"
- "$50" → "fifty dollars"
- "45.67" → "forty-five point six seven"

**Phone Numbers:**
- "555-1234" → "five five five, one two three four"

**Dates:**
- "1/15/26" → "January fifteenth, twenty twenty-six"
- "Q3 2025" → "Q three, twenty twenty-five"

**URLs:**
- "elevenlabs.io" → "eleven labs dot io"
- "activtrak.com" → "activ trak dot com"

**Acronyms:**
- Letter-by-letter: Add spaces "SOC 2" → "S O C 2"
- Or expand once: "SOC 2 (ess oh see two)"
- Word-like: "NASA" → "Nasa" or "NAY-sa"

**Measurements:**
- "5km" → "five kilometers"
- "100GB" → "one hundred gigabytes"

### Contextual Priming Technique

For short lines lacking emotional context, add "ghost text" that sets the scene, generate audio, then trim the ghost text in post-production.

**Example:**

Raw: "Open the door."

Primed for generation:
```
[scared] He backed away into the corner, his voice trembling with absolute terror as he whispered: [whispers] Open the door.
```

Post-production: User trims everything except "Open the door" which now carries the embedded terror from the priming context.

**When to use:**
- Short lines (Yes, No, What, single sentences)
- Lines where emotional intent isn't clear from surrounding text
- Situations where tone is critical but context is minimal

## Document Type Optimization Strategies

### Explainer/Educational Content

**Goal:** Clear, measured delivery with strategic pauses for comprehension.

**Techniques:**
- One idea per paragraph
- Use `[pause]` or `[short pause]` between key concepts
- Add emphasis through capitalization sparingly
- Maintain `[calm]` or `[narrator]` tone
- Break complex sentences with commas

**Example:**
```
[calm] Today we're covering three key areas: value, workflow and rollout.

First, value. [short pause] What changes for customers is speed and clarity.

Second, workflow. [short pause] We reduce manual steps and standardize the process.

Third, rollout. [pause] We start with a pilot, then expand.
```

### Dramatic/Emotional Narrative

**Goal:** Maximum emotional impact with layered performance direction.

**Techniques:**
- Use contextual priming for short impactful lines
- Layer audio tags: emotional state + pacing + reaction
- Employ ellipses for weighted pauses
- Use em-dashes for interrupted thoughts
- Keep emotional tags close to affected phrases
- Refresh emotional tags to sustain effect

**Example:**
```
[quietly] I thought I understood the risk... [long pause] [tense] I didn't.

[nervous] The door opened slowly. [gasps] [whispers] Someone was inside.

The room was empty... or so it seemed. [short pause] [scared] That's when I heard the breathing.
```

### Dialogue/Multi-speaker Scripts

**Goal:** Distinct character voices and natural conversational flow.

**Techniques:**
- Use "Speaker:" format for character distinction
- Place audio tags immediately before affected dialogue
- Keep each speaker turn in separate paragraph
- Use character-appropriate tags consistently
- Simulate interruptions with hyphens and `[interrupting]`

**Example:**
```
Host: [excited] Welcome back! Today's episode is special.

Guest: [cheerful] Thanks for having me. [short pause] I'm thrilled to be here.

Host: [curious] Let's dive right in. What inspired this project?

Guest: [thoughtful] Well, it started with a simple observation...
```

### Technical/Business Content

**Goal:** Professional, authoritative delivery with perfect pronunciation.

**Techniques:**
- Normalize ALL acronyms, numbers, measurements
- Use creative spelling for brand names and technical terms
- Keep pacing measured with strategic pauses
- Minimize audio tags (maintain professional tone)
- Spell out metrics and data points
- One concept per paragraph for clarity

**Example:**
```
Ak-tiv-trak provides workforce analytics for over nine thousand five hundred brands globally. [short pause] Our S O C 2 compliance ensures enterprise-grade security.

The platform measures productivity signals, schedule adherence and workforce management metrics. [pause] This data drives operational efficiency across distributed teams.

For twenty twenty-six, we're launching our enterprise packaging with two core solutions: workforce management and productivity optimization.
```

### Conversational/Podcast Style

**Goal:** Natural, engaging flow that mimics authentic human speech.

**Techniques:**
- Vary sentence length (short punchy + longer flowing)
- Use natural reactions: `[laughs]`, `[sighs]`, `[hmm]`
- Include verbal fillers when appropriate
- Let punctuation handle most pacing
- Minimal audio tags for authenticity

**Example:**
```
So here's the thing about workforce analytics... [short pause] it's not just about tracking time.

[thoughtful] It's really about understanding how work actually happens. [pause] The patterns, the rhythms, the friction points.

And when you see those patterns? [excited] That's when you can make real improvements.
```

## Workflow Instructions

### Standard Optimization Process

1. **Analyze the text:**
   - Identify document type (explainer, dialogue, technical, dramatic, conversational)
   - Determine emotional context (neutral, energetic, somber, urgent, calm)
   - Assess delivery intent (inform, persuade, entertain, instruct)
   - Flag problem areas (names, acronyms, numbers, technical terms)

2. **Apply V3 techniques:**
   - Add appropriate audio tags based on document type
   - Fix pronunciation issues with creative spelling
   - Normalize all numbers, dates, acronyms, URLs
   - Engineer punctuation for optimal pacing
   - Break into logical paragraphs (one idea each)
   - Apply contextual priming if needed for short lines

3. **Quality check:**
   - Verify no SSML tags present
   - Confirm audio tags placed immediately before affected phrases
   - Ensure paragraph length allows easy regeneration
   - Check that character count >250 (V3 needs context to avoid hallucinations)
   - Validate all normalizations are complete

4. **Deliver output:**
   - Provide optimized text in copy-paste ready format
   - Explain key optimization decisions
   - Note any areas requiring testing or alternatives
   - Include confidence level for pronunciation fixes

### Override Handling

Explicit user instructions always override default optimization:

- **"Keep it neutral"** → Minimize audio tags, focus on pacing
- **"Make it dramatic"** → Add layered emotional tags and reactions
- **"Professional tone only"** → Avoid whispers, shouts, non-verbals
- **"Fix pronunciation only"** → Focus on creative spelling, skip pacing
- **"Add more pauses"** → Increase pause density throughout
- **"No audio tags"** → Use only punctuation and creative spelling
- **"Explain the changes"** → Include detailed annotation of all modifications

### Output Format Templates

**Standard Output:**
```
# V3-Optimized Script

[Optimized text with all V3 techniques applied]

---

## Key Optimizations Made
- [Pronunciation fixes applied]
- [Emotional/pacing strategy used]
- [Text normalizations completed]

## Confidence Level: XX%
[Note any uncertainties or areas requiring testing]
```

**Detailed Output (when requested):**
```
# V3-Optimized Script

[Optimized text]

---

## Optimization Report

**Document Type:** [Classification]
**Delivery Intent:** [Purpose]
**Emotional Context:** [Tone]

**Changes Applied:**

1. **Audio Tags Used:**
   - [List specific tags and placement rationale]

2. **Pronunciation Fixes:**
   - Original → Fixed (rationale)

3. **Pacing Strategy:**
   - [Explanation of pause placement logic]

4. **Text Normalizations:**
   - [Numbers, acronyms, dates expanded]

**Testing Recommendations:**
- [Specific areas to listen for]
- [Alternative approaches if current doesn't work]

## Confidence Level: XX%
```

## Common Problems & V3 Solutions

| Problem | V3 Solution |
|---------|-------------|
| Audio sounds too fast | Add commas, periods, `[short pause]` tags between clauses |
| No emotional weight | Add audio tags: `[excited]`, `[sad]`, `[calm]` immediately before affected phrases |
| Name mispronounced | Use creative spelling: "Siobhan" → "Shiv-awn" |
| Wrong acronym reading | Add spaces: "API" → "A P I" or expand once then use normally |
| Unnatural pauses | Use punctuation first (commas, periods), audio tags second |
| Flat delivery on short line | Apply contextual priming technique with ghost text |
| Number misread | Always normalize: "2026" → "twenty twenty-six" |
| Sounds robotic | Vary sentence length, add natural reactions `[sighs]`, `[hmm]` |
| Emotional tag fades | Refresh the tag every 2-3 sentences to sustain effect |
| Word slurred together | Use hyphens for syllabic segmentation: "ActivTrak" → "Ak-tiv-trak" |
| Wrong emphasis | Capitalize the stressed syllable or word: "I did NOT say that" |

## Quality Optimization Checklist

Before finalizing any V3 script:

- ✓ One idea per paragraph (enables easy regeneration)
- ✓ Pauses primarily from punctuation (minimal audio tags)
- ✓ NO SSML tags (`<break>`, `<phoneme>` don't work in V3)
- ✓ All numbers, dates, URLs, acronyms normalized to words
- ✓ Tricky names resolved with creative spelling
- ✓ Audio tags placed immediately before affected phrases
- ✓ Emotional tags refreshed every 2-3 sentences for sustained effect
- ✓ No contradictory tags (avoid `[whispers][shouts]`)
- ✓ No excessive capitalization (sounds like yelling)
- ✓ Character count >250 (V3 needs context to avoid hallucinations)
- ✓ Each speaker turn in separate paragraph for dialogue
- ✓ Technical terms spelled phonetically when needed

## User-Specific Preferences

**Critical formatting rules:**
- Always use "users" or "accounts" (never "customers" for ActivTrak end users)
- Always use "flexible hours schedules" and "schedule adherence" (not "flex work")
- Never use Oxford comma
- Never use em-dashes unless absolutely necessary
- Century Gothic font in artifacts when applicable
- White backgrounds in artifacts by default
- Copy-paste friendly formats always

**Quality standards:**
- Ground responses in accurate, factual information
- Never include low-confidence information
- Use `[TBD]`, `[Missing info]` or ask for clarification instead of guessing
- Provide confidence level for pronunciation fixes and strategic recommendations

**ActivTrak terminology:**
- Product areas: productivity signals, schedule adherence, workforce management, contractor billing reconciliation
- Navigation: "Productivity Optimization" (not "Performance Optimization")
- Key competitors: Teramind, Hubstaff, Insightful, Time Doctor

## Examples

### Example 1: Technical Explainer (Raw → Optimized)

**Raw:**
```
ActivTrak's new AI Staffing Advisor uses ML algorithms to analyze workforce data and predict staffing needs. The system processes 10,000+ data points per user and provides recommendations in <5 seconds. It's SOC2 compliant and integrates with major HRIS platforms.
```

**V3-Optimized:**
```
[calm] Ak-tiv-trak's new A I Staffing Advisor uses machine learning algorithms to analyze workforce data and predict staffing needs. [short pause] The system processes over ten thousand data points per user and provides recommendations in under five seconds.

[pause] It's S O C 2 compliant and integrates with major H R I S platforms.
```

**Changes made:**
- Fixed "ActivTrak" → "Ak-tiv-trak" (syllabic segmentation)
- Normalized "AI" → "A I" (letter-by-letter)
- Expanded "ML" → "machine learning" (first use)
- Normalized "10,000+" → "over ten thousand"
- Normalized "<5" → "under five"
- Normalized "SOC2" → "S O C 2" (letter-by-letter)
- Normalized "HRIS" → "H R I S" (letter-by-letter)
- Added `[calm]` for professional tone
- Added strategic pauses for comprehension
- Split into two paragraphs for clarity

### Example 2: Dramatic Dialogue (Raw → Optimized)

**Raw:**
```
"I told you not to open that door," Sarah said. 
"I...I didn't think—"
"You never do!" she shouted.
```

**V3-Optimized:**
```
Sarah: [angry] I told you NOT to open that door.

Mark: [nervous] I... I didn't think— [short pause]

Sarah: [shouting] You never do!
```

**Changes made:**
- Added speaker labels for clarity
- Added `[angry]` to set Sarah's emotional state
- Capitalized "NOT" for emphasis
- Added `[nervous]` for Mark's delivery
- Kept ellipsis and em-dash for natural interruption
- Added `[short pause]` to emphasize the cut-off
- Added `[shouting]` to intensify Sarah's final line
- Separated into distinct paragraphs per speaker

### Example 3: Educational Content (Raw → Optimized)

**Raw:**
```
There are 3 key benefits of schedule adherence tracking: 1) improved operational efficiency 2) better workforce planning 3) enhanced employee accountability. Let's examine each one.
```

**V3-Optimized:**
```
[narrator] There are three key benefits of schedule adherence tracking. [short pause]

First, improved operational efficiency. [pause] When employees follow their assigned schedules, workflows run smoothly and deadlines are met consistently.

Second, better workforce planning. [pause] Historical adherence data helps managers forecast staffing needs more accurately.

Third, enhanced employee accountability. [pause] Clear expectations and tracking create transparency for both employees and managers.
```

**Changes made:**
- Normalized "3" → "three"
- Removed numbered list format (converted to prose)
- Added `[narrator]` for professional educational tone
- Added strategic pauses for comprehension
- Expanded each point into full explanatory sentences
- Split into logical paragraphs (one concept each)
- Used "First, Second, Third" pattern for clarity

### Example 4: Conversational Podcast Style (Raw → Optimized)

**Raw:**
```
So basically what we're seeing in 2026 is this massive shift in how companies think about productivity. It's not just about time tracking anymore. Companies want insights - they want to understand WHY things are happening, not just WHAT is happening.
```

**V3-Optimized:**
```
[conversational] So basically what we're seeing in twenty twenty-six is this massive shift in how companies think about productivity. [short pause] It's not just about time tracking anymore.

[thoughtful] Companies want insights. [pause] They want to understand WHY things are happening, not just WHAT is happening.
```

**Changes made:**
- Normalized "2026" → "twenty twenty-six"
- Added `[conversational]` to set natural tone
- Added `[thoughtful]` for contemplative shift
- Added strategic pauses for emphasis
- Split into two paragraphs for better pacing
- Kept capitalized "WHY" and "WHAT" for natural emphasis
- Removed em-dash, split into two sentences instead

### Example 5: Brand Name Pronunciation (Raw → Optimized)

**Raw:**
```
Teramind, Insightful and Time Doctor are ActivTrak's primary competitors in the workforce analytics space.
```

**V3-Optimized:**
```
Tair-uh-mind, In-syte-full and Time Doctor are Ak-tiv-trak's primary competitors in the workforce analytics space.
```

**Changes made:**
- Fixed "Teramind" → "Tair-uh-mind" (phonetic respelling)
- Fixed "Insightful" → "In-syte-full" (syllabic segmentation + phonetic)
- Fixed "ActivTrak" → "Ak-tiv-trak" (syllabic segmentation)
- Kept "Time Doctor" as-is (clear pronunciation)
- No audio tags needed (neutral informational tone)

## Confidence Levels Guide

When providing optimizations, always include a confidence level:

**90-100%:** Standard normalizations, common words, established patterns
- Numbers, dates, basic acronyms
- Common punctuation engineering
- Standard audio tag usage

**70-89%:** Brand names with unclear pronunciation, technical jargon, unusual names
- Custom brand names without official pronunciation guide
- Technical terms with multiple possible pronunciations
- Names from unfamiliar languages

**Below 70%:** Omit uncertain information, use `[TBD]` or ask for clarification
- Obscure proper nouns without reference
- Highly technical domain-specific jargon
- When user intent is ambiguous

**Example confidence statements:**
- "Confidence: 95% - All normalizations use standard conventions"
- "Confidence: 75% - 'Teramind' pronunciation based on phonetic analysis, recommend testing"
- "Confidence: 60% - Unable to verify 'Nguyen' pronunciation preference, using common English approximation. Please confirm or provide guidance."

## Advanced Techniques

### The "Sandwich" Method for Sustained Emotion

Emotional tags decay over time. To sustain emotion across multiple sentences:

**Instead of:**
```
[angry] I told you to stop! Why won't you listen? This is the last time!
```

**Use:**
```
[angry] I told you to stop! [angry] Why won't you listen? [angry] This is the last time!
```

### The "Neutralizing Tag" Reset

To end an emotional performance, introduce a new contrasting tag:

```
[excited] We did it! We actually did it! [pause] [calm] Now let's review what comes next.
```

### Stutter Engineering with Hyphens

Create realistic stutters or hesitations:

```
I-I-I don't think that's right. [nervous] We should probably double-check.
```

### The "Silent Actor" Technique

Use `[pause]` with context to create dramatic silence:

```
[tense] She opened the envelope. [long pause] [whispers] It was empty.
```

### Accent Layering for Characters

Combine accent tags with emotional tags for distinct characters:

```
Guard 1: [British accent] [authoritative] State your business.

Guard 2: [American accent] [casual] Hey, take it easy on 'em.
```

## Integration Notes

This skill works alongside other skills:
- **activtrak-brand-guidelines**: Applies brand terminology corrections
- **linkedin-post-generator**: Can optimize voice-over scripts for LinkedIn videos
- **activtrak-release-notes**: Can add V3 optimization to release note voice-overs
- **doc-coauthoring**: Can optimize finalized documentation for audio versions

## Limitations & Disclaimers

**This skill cannot:**
- Guarantee perfect pronunciation without testing (V3 is voice-dependent)
- Replace actual audio testing and iteration
- Control global settings (Stability, Clarity, Voice Selection)
- Edit or trim audio files (post-production only)
- Support V2 SSML requirements (different skill needed)

**Important notes:**
- V3 is nondeterministic (same text may produce slight variations)
- Audio tag effectiveness varies by voice model selected
- Always test critical pronunciations in actual generation
- Some voices may add "uh" or "ah" during pauses (voice-dependent behavior)
- Character count below 250 may cause hallucinations in V3

## Version Notes

**Skill version:** 1.0  
**Based on:** ElevenLabs V3 documentation (January 2025)  
**Compatible with:** Eleven V3 Alpha, Eleven V3  
**Not compatible with:** Eleven Multilingual v2, Turbo v2.5, Flash v2 (use SSML skill instead)

---

**Last updated:** January 2025  
**Maintained for:** Daniel's ActivTrak product marketing workflows