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:

Stability: 0-1 (lower = more expressive, higher = more consistent)

Similarity boost: 0-1 (higher = closer to original voice)

Style: 0-1 (higher = more exaggerated style)

For natural speech:

stability = 0.5 # Balanced expression similarity = 0.75 # Close to voice but natural style = 0.0 # Neutral (increase for dramatic)

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:

isolate_audio to clean source material voice_clone with cleaned audio Test with varied prompts Adjust stability/similarity for output quality Voice Processing Chain

Standard voice chain (order matters!):

[Raw Recording] ↓ [High-Pass Filter @ 80Hz] ← Remove rumble, plosives ↓ [De-esser @ 5-8kHz] ← Before compression! ↓ [Compressor 3:1, 10ms/100ms] ← Smooth dynamics ↓ [EQ: +2dB @ 3kHz presence] ← Clarity boost ↓ [Limiter -1 dBTP] ← Prevent clipping ↓ [Loudness Norm -16 LUFS] ← Target loudness

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:

create_agent( name="Support Agent", first_message="Hi, how can I help you today?", system_prompt="You are a helpful customer support agent...", voice_id="your_voice_id", language="en", llm="gemini-2.0-flash-001", # Fast for conversation temperature=0.5, asr_quality="high", # Speech recognition quality turn_timeout=7, # Seconds before agent responds max_duration_seconds=300 # 5 minute call limit )

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 De-esser: 5-8kHz, -6dB reduction, Q=2 Compressor: 3:1 ratio, -20dB threshold, 10ms attack, 100ms release EQ presence: +2-3dB shelf at 3kHz HPF: 80-100Hz, 12dB/oct Limiter: -1 dBTP ceiling

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

1. Pre-processing:

Normalize speech rate before ASR

Use librosa to stretch irregular segments toward target rate

import librosa y, sr = librosa.load("disfluent.wav") y_stretched = librosa.effects.time_stretch(y, rate=0.9) # Slow down

1. 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

1. Fine-tuning Whisper (advanced):

Fine-tune on disfluent speech dataset

Datasets: FluencyBank, UCLASS, SEP-28k (stuttering)

from transformers import WhisperForConditionalGeneration, WhisperProcessor

model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")

Fine-tune on your speech samples with corrected transcripts

Training loop with disfluent audio → fluent transcript pairs

1. 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.