voice-audio-engineer

✅ 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 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 |

| 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 |

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

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

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:

```python

# 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:

```python

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

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

```

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:

```python

# 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):

```python

# 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

| 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 |

• 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.