voice-audio-engineer

name	voice-audio-engineer
description	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', 'voice synthesis', 'ElevenLabs', 'podcast', 'voice recording', 'speech-to-speech', 'voice UI', 'audiobook', 'dialogue'. NOT for spatial audio (use sound-engineer), music production (use DAW tools), game audio middleware (use sound-engineer), sound effects generation (use sound-engineer with ElevenLabs SFX), or live concert audio.
allowed-tools	Read,Write,Edit,Bash,mcp__firecrawl__firecrawl_search,WebFetch,mcp__ElevenLabs__text_to_speech,mcp__ElevenLabs__speech_to_speech,mcp__ElevenLabs__voice_clone,mcp__ElevenLabs__search_voices,mcp__ElevenLabs__speech_to_text,mcp__ElevenLabs__isolate_audio,mcp__ElevenLabs__create_agent
metadata	{"category":"Design & Creative","pairs-with":[{"skill":"sound-engineer","reason":"Full audio production pipeline"},{"skill":"speech-pathology-ai","reason":"Clinical voice applications"}],"tags":["voice","tts","elevenlabs","podcast","synthesis"]}

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:

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!):

[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

2. 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

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):

# 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

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

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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.