Expert in voice synthesis, TTS, voice cloning, podcast production, speech processing, and voice UI design via ElevenLabs integration. Specializes in vocal clarity, loudness standards (LUFS), de-essing, dialogue mixing, and voice transformation. Activate on 'TTS', 'text-to-speech', 'voice clone', 'vo

Voice & Audio Engineer: Voice Synthesis, TTS & Speech Processing Expert in voice synthesis, speech processing, and vocal production using ElevenLabs and professional audio techniques. Specializes in TTS, voice cloning, podcast production, and voice UI design. When to Use This Skill ✅ Use for: Text to speech (TTS) generation Voice cloning and voice design Speech to speech voice transformation Podcast production and editing Audiobook production Voice UI/conversational AI audio Dialogue mixing and processing Loudness normalization (LUFS) Voice quality enhancement (de essing, compression) Transcription and speech to text ❌ Do NOT use for: Spatial audio (HRTF, Ambisonics) → sound engineer Sound effects generation → sound engineer (ElevenLabs SFX) Game audio middleware (Wwise, FMOD) → sound engineer Music composition/production → DAW tools Live concert/event audio → specialized domain MCP Integrations MCP Tool Purpose text to speech Generate speech from text with voice selection speech to speech Transform voice recordings to different voices voice clone Create instant voice clones from audio samples search voices Find voices in ElevenLabs library speech to text Transcribe audio with speaker diarization isolate audio Separate voice from background noise create agent Build conversational AI agents with voice Expert vs Novice Shibboleths Topic Novice Expert TTS quality "Any voice works" Matches voice to brand; considers emotion, pace, style Voice cloning "Upload any audio" Knows 30s 3min of clean, varied speech needed; single speaker Loudness "Make it loud" Targets 16 to 19 LUFS for podcasts; 14 for streaming De essing "Doesn't matter" Knows sibilance lives at 5 8kHz; frequency selective compression Compression "Squash it" Uses 3:1 4:1 for dialogue; slow attack (10 20ms) to preserve transients High pass "Never use it" Always HPF at 80 100Hz for voice; removes rumble, plosives True peak "Peak is peak" Knows intersample peaks exceed 0dBFS; targets 1 dBTP ElevenLabs models "Use default" eleven multilingual v2 for quality; eleven flash v2 5 for speed Common Anti Patterns Anti Pattern: Uploading Noisy Audio for Voice Cloning What it looks like : Voice clone from phone recording with background noise, echo Why it's wrong : Clone learns the noise; output has artifacts What to do instead : Use isolate audio first; record in quiet space; provide 1 3 min of varied speech Anti Pattern: Ignoring Loudness Standards What it looks like : Podcast at 6 LUFS, then normalized by platform → crushed dynamics Why it's wrong : Each platform normalizes differently; too loud = distortion, too quiet = inaudible What to do instead : Master to 16 LUFS for podcasts; 14 LUFS for streaming; always check true peak < 1 dBTP Anti Pattern: TTS Without Voice Matching What it looks like : Using default robotic voice for premium product Why it's wrong : Voice IS brand; wrong voice = wrong emotional connection What to do instead : search voices to find matching tone; consider custom clone for brand consistency Anti Pattern: No De essing on Processed Voice What it looks like : "SSSSibilant" speech after compression and EQ boost Why it's wrong : Compression brings up sibilance; EQ boost at 3 5kHz makes it worse What to do instead : De ess at 5 8kHz before compression; use frequency selective compression Anti Pattern: Single Take, No Editing What it looks like : Podcast with 20 "ums", breath sounds, long pauses Why it's wrong : Listeners fatigue; unprofessional; reduces engagement What to do instead : Edit out filler words; gate or manually cut breaths; tighten pacing Evolution Timeline Pre 2020: Robotic TTS Concatenative synthesis (spliced recordings) Obvious robotic quality Limited voice options 2020 2022: Neural TTS Emerges Tacotron, WaveNet improve naturalness Still detectable as synthetic Voice cloning requires hours of data 2023 2024: AI Voice Revolution ElevenLabs instant voice cloning (30 seconds) Near human quality in TTS Real time voice transformation Voice agents for customer service 2025+: Current Best Practices Emotional TTS (control tone, pace, emotion) Cross lingual voice cloning Real time voice transformation in apps Personalized voice agents Voice authentication integration Core Concepts ElevenLabs Voice Selection Model comparison: Model Quality Latency Languages Use Case eleven multilingual v2 Best Higher 29 Production, quality critical eleven flash v2 5 Good Lowest 32 Real time, voice UI eleven turbo v2 5 Better Low 32 Balanced Voice parameters: Voice Cloning Best Practices Audio requirements: Duration: 1 3 minutes (more = better, diminishing returns after 3min) Quality: Clean, no background noise, no reverb Content: Varied speech (questions, statements, emotions) Format: WAV/MP3, 44.1kHz or higher Cloning workflow: 1. isolate audio to clean source material 2. voice clone with cleaned audio 3. Test with varied prompts 4. Adjust stability/similarity for output quality Voice Processing Chain Standard voice chain (order matters!): Loudness Standards Platform/Format Target LUFS True Peak Podcast 16 to 19 1 dBTP Audiobook (ACX) 18 to 23 RMS 3 dBFS YouTube 14 1 dBTP Spotify/Apple Music 14 1 dBTP Broadcast (EBU R128) 23 ±1 1 dBTP Measurement: LUFS = Loudness Units Full Scale (integrated) True Peak = Maximum level including intersample peaks Always measure with K weighting (ITU R BS.1770) Conversational AI Agents ElevenLabs agent configuration: Voice UI considerations: Use fast model ( eleven flash v2 5 ) for real time Keep responses concise (< 30 seconds) Add pauses for natural conversation flow Handle interruptions gracefully Quick Reference Voice Selection Decision Tree Brand/professional content? → Custom clone or curated voice Real time/interactive? → eleven flash v2 5 model Quality critical? → eleven multilingual v2 model Multiple languages? → Check language support per voice Processing Decision Tree Voice sounds muddy? → HPF at 80Hz, boost 3kHz Sibilance harsh? → De ess at 5 8kHz Inconsistent volume? → Compress 3:1, then limit Too quiet? → Normalize to target LUFS Background noise? → Use isolate audio first Common Settings Working With Speech Disfluencies Cluttering vs Stuttering Type Characteristics ASR Impact Stuttering Repetitions ("I I I"), prolongations ("wwwant"), blocks (silent pauses) Word boundaries confused; repetitions misrecognized Cluttering Irregular rate, collapsed syllables, filler overload, tangential speech Words merged; rate changes confuse timing ASR Challenges with Disfluent Speech Most ASR models trained on fluent speech. Disfluencies cause: Word boundary detection errors Repetitions transcribed literally ("I I I want" vs "I want") Collapsed syllables missed entirely Timing models confused by irregular pace Solutions & Workarounds 1. Model selection (best to worst for disfluencies): Whisper large v3 Most robust to disfluencies ElevenLabs speech to text Good with varied speech Google Speech to Text Decent with enhanced models Fast/lightweight models Usually worst 2. Pre processing: 3. Post processing: Remove duplicate words: "I I I want" → "I want" Filter common fillers: "um", "uh", "like", "you know" Use LLM to clean transcripts while preserving meaning 4. Fine tuning Whisper (advanced): 5. ElevenLabs voice cloning approach: Clone your voice from fluent segments Use TTS for fluent output with your voice Great for pre recorded content, not live Accessibility Considerations Always provide manual transcript correction option Consider hybrid: ASR + human review For voice UI: longer timeout, confirmation prompts Test with actual users from target population Performance Targets Operation Typical Time TTS (100 words) 2 5 seconds Voice clone creation 10 30 seconds Speech to speech 3 8 seconds Transcription (1 min audio) 5 15 seconds Audio isolation 5 20 seconds Integrates With sound engineer For spatial audio, game audio, procedural SFX native app designer Voice UI implementation in apps vr avatar engineer Avatar voice integration For detailed implementations : See /references/implementations.md Remember : Voice is intimate—it speaks directly to the listener's brain. Match voice to brand, process for clarity not loudness, and always respect the platform's loudness standards. With ElevenLabs, you have instant access to professional voice synthesis; use it thoughtfully.

voice-audio-engineer