AI Voice Assistant: Build Hands-Free Chat with OpenClaw

Voice assistants like Alexa, Siri, and Google Assistant dominate hands-free computing, but they send every utterance to cloud servers and lock you into proprietary ecosystems. What if you could build your own voice assistant—one that respects privacy, runs locally, and integrates with any AI model you choose?

This guide shows you how to build a fully-functional voice assistant using OpenClaw. You’ll implement wake word detection (“Hey Assistant”), speech-to-text conversion, natural language processing with your choice of AI model (GPT-4, Claude, or local Llama), and natural voice responses—all customizable to your needs. Whether for accessibility, productivity, or hands-free operation, you’ll have a personal voice AI in under an hour.

What You’ll Build

By the end of this guide, you’ll have a voice assistant that:

Activates on wake word: Say “Hey Jarvis” or custom phrase to start listening, just like “Hey Alexa.”

Transcribes speech accurately: Convert your spoken words to text using Whisper AI or cloud services (Google, Azure).

Understands natural language: Process requests using GPT-4, Claude, local Llama, or any supported AI model with full conversational context.

Responds with natural voice: Convert text responses to speech using ElevenLabs, OpenAI TTS, or local engines. Choose voice personality, accent, and speed.

Works hands-free: Operate entirely via voice—no keyboard or screen needed. Perfect for cooking, driving, accessibility, or multitasking.

Runs locally (optional): Full privacy mode using local wake word detection (Porcupine), local speech recognition (Whisper), local LLMs (Ollama), and local TTS (Piper). Zero cloud dependencies.

Integrates with your life: Control smart home devices, manage calendar, send messages, search the web, or any OpenClaw skill—all via voice.

Why Build Your Own Voice Assistant?

Privacy Control

Commercial voice assistants record and transmit every conversation to company servers. Amazon, Google, and Apple have admitted employees listen to recordings for “quality assurance.” Data persists indefinitely on their systems.

Your self-built assistant keeps data local. Conversations never leave your device (when using local models), no corporate servers analyze your speech, no employee eavesdropping on recordings, and you control what’s logged and for how long. For sensitive conversations—financial planning, medical discussions, confidential business—self-hosted voice is the only privacy-respecting option.

Complete Customization

Commercial assistants have fixed personalities, limited wake words (“Alexa” only), and restricted capabilities (what Amazon/Google allow). You can’t change fundamental behavior, add advanced features requires their approval, or integrate with tools they don’t support.

Your assistant is fully customizable—choose any wake word (“Hey Jarvis,” “Computer,” your name), select voice personality and accent, implement any capability via OpenClaw skills, and modify behavior and response patterns, integrate with any API or service.

Cost Savings

Many cloud voice services charge per request. Google Cloud Speech-to-Text costs $0.006 per 15 seconds (~$25/month for heavy use). Premium TTS like ElevenLabs costs $5-$100/month depending on characters.

Self-hosted voice has zero recurring costs after initial setup. Use free tiers of cloud services (100 minutes/month free on many platforms) or run completely local with Whisper + Piper TTS + Ollama LLM for $0 monthly. Hardware you likely already own works fine.

Accessibility

Voice interfaces remove barriers for people with visual impairments, mobility limitations, dyslexia or reading difficulties, repetitive strain injuries, or those multitasking (cooking, driving, childcare). A well-designed voice assistant dramatically improves computer access for diverse users.

Commercial solutions offer some accessibility features but with privacy trade-offs and limited customization. Your self-built assistant can be tailored to specific needs—speech patterns, response pacing, command structures—without compromising personal data.

Learning and Control

Building your own voice assistant teaches valuable skills in speech processing, natural language understanding, audio engineering, and system integration. You understand how the technology works rather than treating it as magic black box. When issues arise, you can debug and fix them rather than waiting for vendor support.

Architecture Overview

A voice assistant has five core components:

[Microphone] → [Wake Word Detection] → [Speech-to-Text]
    ↓                                        ↓
[Speaker] ← [Text-to-Speech] ← [AI Processing (LLM)]

1. Wake Word Detection: Continuously listens for activation phrase. Low-power, always-on process that triggers full system when detected.

2. Speech-to-Text (STT): Converts spoken audio to written text. Most resource-intensive component—requires fast processing to feel responsive.

3. AI Processing: OpenClaw processes transcribed text using configured AI model (GPT-4, Claude, Llama, etc.). Same as text-based conversation but triggered by voice.

4. Text-to-Speech (TTS): Converts AI response from text to natural audio. Quality varies dramatically between engines.

5. Audio I/O: Microphone captures user speech, speaker plays assistant responses. Proper audio setup critical for good experience.

Let’s build each component step by step.

Prerequisites

Hardware Requirements

Microphone: Any USB microphone, laptop built-in mic, or Bluetooth headset. Better mic = more accurate transcription. Budget: $15-50 for decent USB mic (Blue Snowball, Samson Go).

Speaker: Laptop speakers work but dedicated speaker or headphones provide better experience. Budget: $20-100 for decent speaker.

Computer: Any modern computer (Windows, macOS, Linux). Raspberry Pi 4/5 works for lightweight setup. Requirements depend on whether using cloud or local speech processing.

Optional - For local processing: See local LLM guide for hardware specs. Summary: 16GB+ RAM preferred, GPU helpful but not required.

Software Prerequisites

OpenClaw installed:

npm install -g openclaw

Python 3.8+ (for Whisper if using local STT):

python3 --version

Node.js 18+ (for OpenClaw):

node --version

Step 1: Basic Setup (Text Mode First)

Before adding voice, ensure OpenClaw works in text mode.

Initialize Project

mkdir voice-assistant
cd voice-assistant
openclaw init

# Install terminal platform for testing
openclaw add platform terminal

Configure Basic AI

Edit openclaw.config.yaml:

name: voice-assistant
version: 1.0.0

ai:
  provider: anthropic  # or 'openai', 'ollama'
  model: claude-3-5-haiku-20241022  # Fast for voice
  temperature: 0.7
  max_tokens: 150  # Shorter responses for voice

platforms:
  - type: terminal
    enabled: true

Test Text Mode

openclaw start

# In terminal, type test message
You: What's the weather today?

If working correctly, you’ll get AI response. Now add voice.

Step 2: Enable Speech-to-Text (STT)

OpenClaw supports multiple STT engines. Choose based on your priorities.

Option A: OpenAI Whisper (Best Accuracy, Cloud)

Pros: Industry-leading accuracy, supports 100+ languages, handles accents well, reasonable pricing ($0.006 per minute).

Setup:

Install Whisper support:

npm install @openai/whisper

Update openclaw.config.yaml:

voice:
  stt:
    provider: openai-whisper
    model: whisper-1
    language: en  # or 'auto' for auto-detection

  audio:
    sample_rate: 16000
    channels: 1

Add API key to .env:

OPENAI_API_KEY=sk-your-key-here

Option B: Google Cloud Speech-to-Text (Free Tier, Good Quality)

Pros: 60 minutes free per month, good accuracy, fast, supports 125 languages.

Setup:

npm install @google-cloud/speech

Get credentials from Google Cloud Console → Enable Speech-to-Text API → Create service account → Download JSON key.

Update config:

voice:
  stt:
    provider: google-cloud
    language_code: en-US
    credentials_path: ./google-credentials.json

Option C: Local Whisper (Maximum Privacy, Free)

Pros: Completely free, fully private, works offline, no API limits. Cons: Requires more powerful hardware, slower than cloud.

Setup:

Install Whisper locally:

pip install openai-whisper

Update config:

voice:
  stt:
    provider: whisper-local
    model: base  # or 'small', 'medium', 'large'
    device: cpu  # or 'cuda' for GPU

Model sizes:

• tiny: Fastest, least accurate (~39MB)
• base: Good balance (~74MB)
• small: Better accuracy (~244MB)
• medium: High accuracy (~769MB, slow on CPU)
• large: Best accuracy (~1550MB, GPU recommended)

Test STT

openclaw voice test-stt

# Speak into microphone
# You should see transcribed text appear

Step 3: Enable Text-to-Speech (TTS)

Choose TTS engine based on voice quality needs and budget.

Option A: ElevenLabs (Best Quality, Premium)

Pros: Most natural voices, emotional intonation, celebrity voice cloning. Pricing: Free tier 10k characters/month, then $5-$100/month.

Setup:

npm install elevenlabs

Get API key from elevenlabs.io.

Update config:

voice:
  tts:
    provider: elevenlabs
    voice_id: EXAVITQu4vr4xnSDxMaL  # Rachel voice
    model: eleven_monolingual_v1
    stability: 0.5
    similarity_boost: 0.75

Add to .env:

ELEVENLABS_API_KEY=your-key-here

Option B: OpenAI TTS (Good Quality, Affordable)

Pros: Natural voices, reasonable pricing ($0.015 per 1K characters), easy integration.

Setup:

Update config:

voice:
  tts:
    provider: openai
    model: tts-1  # or 'tts-1-hd' for higher quality
    voice: alloy  # alloy, echo, fable, onyx, nova, shimmer
    speed: 1.0  # 0.25-4.0

Uses same OPENAI_API_KEY as Whisper.

Option C: Google Cloud TTS (Free Tier, Decent Quality)

Pros: 1 million characters free per month, many voices, 40+ languages.

Setup:

npm install @google-cloud/text-to-speech

Update config:

voice:
  tts:
    provider: google-cloud
    language_code: en-US
    voice_name: en-US-Neural2-C  # Female voice
    speaking_rate: 1.0
    pitch: 0.0

Option D: Piper (Local, Free, Open Source)

Pros: Completely free, private, offline, lightweight. Cons: Less natural than premium cloud services.

Setup:

Install Piper:

# Linux
sudo apt install piper-tts

# macOS
brew install piper-tts

# Or via pip
pip install piper-tts

Download voice model:

# Download voice (many available)
wget https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-amy-low.tar.gz
tar -xzf voice-en-us-amy-low.tar.gz

Update config:

voice:
  tts:
    provider: piper
    model_path: ./voice-en-us-amy-low.onnx
    speaker: 0

Test TTS

openclaw voice test-tts "Hello, this is a test of text to speech."

# You should hear voice output

Step 4: Wake Word Detection

Wake word detection allows hands-free activation (“Hey Assistant”). System listens continuously but only processes speech after wake word detected.

Option A: Porcupine (Best Free Option)

Picovoice Porcupine offers accurate wake word detection with free tier (3 wake words, unlimited usage).

Setup:

npm install @picovoice/porcupine-node

Create account at console.picovoice.ai → Get Access Key → Create custom wake word or use built-in (“Jarvis”, “Computer”, “Hey Siri”).

Update config:

voice:
  wake_word:
    provider: porcupine
    access_key: your-porcupine-key
    keywords:
      - jarvis  # or custom wake word
    sensitivity: 0.5  # 0-1 (higher = more sensitive)

Option B: Snowboy (Local, Open Source)

Pros: Completely local, no API, custom wake words. Cons: Less accurate than Porcupine, project archived (use at own risk).

Setup:

npm install snowboy

Train custom wake word at snowboy.kitt.ai.

Update config:

voice:
  wake_word:
    provider: snowboy
    model_path: ./hey-jarvis.pmdl
    sensitivity: 0.5
    audio_gain: 1.0

Option C: No Wake Word (Push-to-Talk)

For simpler setup or privacy, use push-to-talk instead of always-listening.

Update config:

voice:
  wake_word:
    provider: none
    activation: push-to-talk  # Press key to activate
    hotkey: space  # or 'ctrl+space', 'f12', etc.

Step 5: Complete Voice Configuration

Combine all components into full voice assistant.

Complete openclaw.config.yaml

name: voice-assistant
version: 1.0.0

# AI Configuration
ai:
  provider: anthropic
  model: claude-3-5-haiku-20241022
  temperature: 0.7
  max_tokens: 150  # Keep responses concise for voice

# Voice Configuration
voice:
  enabled: true

  # Wake Word
  wake_word:
    provider: porcupine
    access_key: ${PORCUPINE_KEY}
    keywords:
      - jarvis
    sensitivity: 0.5

  # Speech-to-Text
  stt:
    provider: openai-whisper
    model: whisper-1
    language: auto

  # Text-to-Speech
  tts:
    provider: openai
    model: tts-1
    voice: alloy
    speed: 1.0

  # Audio Settings
  audio:
    input_device: default  # or specific device ID
    output_device: default
    sample_rate: 16000
    channels: 1
    silence_threshold: 500  # ms of silence = end of speech
    max_recording_time: 30  # max seconds per utterance

  # Conversation Settings
  conversation:
    confirmation_sound: true  # Beep when listening
    thinking_indicator: true  # Say "thinking..." while processing
    interrupt_enabled: true  # Allow interrupting responses

# Platforms (voice replaces terminal)
platforms:
  - type: voice
    enabled: true

# Optional: Still enable terminal for debugging
  - type: terminal
    enabled: true

Environment Variables

Complete .env file:

# AI Provider
ANTHROPIC_API_KEY=sk-ant-your-key-here
# or OPENAI_API_KEY=sk-your-key-here

# Wake Word
PORCUPINE_KEY=your-porcupine-access-key

# STT (if using OpenAI Whisper)
# Uses same OPENAI_API_KEY

# TTS (if using ElevenLabs)
ELEVENLABS_API_KEY=your-elevenlabs-key

# Google Cloud (if using Google services)
GOOGLE_APPLICATION_CREDENTIALS=./google-credentials.json

Step 6: Testing and Refinement

Start Voice Assistant

openclaw start --voice

# You should see:
# [Voice] Listening for wake word "jarvis"...
# [Voice] Wake word detected!
# [Voice] Listening... (speak now)
# [STT] Transcribed: "What's the weather in Tokyo?"
# [AI] Processing...
# [TTS] Speaking response...

Test Conversation Flow

Basic Query:

You: "Hey Jarvis"
System: [Beep]
You: "What's 15 plus 27?"
Assistant: "15 plus 27 equals 42."

Multi-Turn Conversation:

You: "Hey Jarvis"
You: "Set a reminder for tomorrow at 2pm"
Assistant: "I've set a reminder for tomorrow at 2pm. What should I remind you about?"
You: "Doctor appointment"
Assistant: "Got it. I'll remind you about your doctor appointment tomorrow at 2pm."

Interruption Handling (if enabled):

You: "Hey Jarvis"
You: "Tell me about the history of Rome"
Assistant: "Rome was founded in 753 BC according to legend. The Roman Kingdom evolved into the Roman Republic in 509 BC, which then became the—"
You: "Stop" or [say wake word again]
Assistant: [Stops speaking] "How can I help?"

Troubleshooting Common Issues

Wake word not detecting:

• Check microphone is default input device
• Adjust sensitivity in config (increase = more sensitive)
• Reduce background noise
• Move closer to microphone
• Test with openclaw voice test-wakeword

Poor transcription accuracy:

• Use better microphone
• Reduce background noise
• Speak clearly and at moderate pace
• Try different STT provider (Whisper usually most accurate)
• Check audio input levels (not too quiet or too loud)

Slow response times:

• Use faster AI model (Haiku instead of Opus/GPT-4)
• Use cloud STT instead of local (faster processing)
• Reduce max_tokens in AI config (shorter responses)
• Check internet connection speed (for cloud services)

Unnatural voice output:

• Try different TTS provider (ElevenLabs > OpenAI > Google > Piper for quality)
• Adjust speaking rate and pitch
• Try different voices within provider
• For ElevenLabs, adjust stability and similarity_boost

Audio feedback/echo:

• Use headphones instead of speakers
• Reduce speaker volume
• Enable echo cancellation in audio settings
• Increase distance between mic and speaker

Step 7: Adding Advanced Features

Skill Integration

Enable OpenClaw skills for voice control:

skills:
  - name: web-search
    enabled: true
  - name: calendar
    enabled: true
  - name: reminders
    enabled: true
  - name: smart-home
    enabled: true

Voice commands automatically work:

You: "Hey Jarvis, search the web for best pizza in Seattle"
You: "Add lunch meeting to my calendar tomorrow at noon"
You: "Turn off living room lights"

For skill installation: openclaw add skill [name]. See Top 10 Skills guide.

Emotion Detection and Response Adaptation

Analyze speech emotion and adapt responses:

voice:
  emotion_detection:
    enabled: true
    provider: openai  # Analyzes tone from audio or text

ai:
  adaptive_personality:
    enabled: true
    modes:
      happy: "Be enthusiastic and energetic"
      sad: "Be empathetic and supportive"
      angry: "Be calm and understanding"
      neutral: "Be helpful and professional"

Voice Command Shortcuts

Create quick voice commands for common tasks:

voice:
  shortcuts:
    - trigger: "daily briefing"
      action: |
        Give me:
        - Today's weather
        - Calendar for today
        - Top 3 news headlines
        - Any reminders

    - trigger: "goodnight"
      action: |
        - Turn off all lights
        - Set alarm for 7am
        - Play sleep sounds

    - trigger: "work mode"
      action: |
        - Open work calendar
        - Check emails
        - Show task list
        - Focus mode on (block distractions)

Multi-Language Support

Support multiple languages with auto-detection:

voice:
  stt:
    language: auto  # Auto-detect
    supported_languages:
      - en
      - es
      - fr
      - de
      - ja
      - zh

  tts:
    provider: google-cloud  # Best multi-language support
    auto_match_language: true  # Respond in detected language

ai:
  instructions: |
    You are a multilingual assistant. Respond in the same language
    the user speaks. If unclear, ask which language they prefer.

Continuous Conversation Mode

Stay active for follow-up questions without repeating wake word:

voice:
  conversation_mode:
    enabled: true
    timeout: 30  # Stay active for 30 seconds after last speech
    max_turns: 10  # Then require wake word again

Usage:

You: "Hey Jarvis"
You: "What's the capital of France?"
Assistant: "The capital of France is Paris."
[Stays listening for 30 seconds]
You: "What's the population?"
Assistant: "Paris has a population of approximately 2.1 million."
You: "And what about London?"
Assistant: "London has a population of about 9 million."

Real-World Use Cases

Use Case 1: Cooking Assistant

Setup: Kitchen tablet/Raspberry Pi with speaker, OpenClaw voice assistant

Configuration:

voice:
  enabled: true
  wake_word: "hey chef"

skills:
  - timer
  - unit-converter
  - recipe-lookup
  - shopping-list

Example interaction:

You: "Hey Chef, convert 250 grams to ounces"
Assistant: "250 grams is approximately 8.8 ounces."

You: "Set a timer for 25 minutes"
Assistant: "Timer set for 25 minutes. I'll notify you when it's done."

You: "Add milk to shopping list"
Assistant: "Added milk to your shopping list."

Benefits: Hands stay clean while cooking, quick conversions and timers, recipe lookup without touching devices.

Use Case 2: Accessibility Aid

Setup: Desktop voice assistant for user with vision impairment

Configuration:

voice:
  enabled: true
  tts:
    speed: 0.9  # Slightly slower for clarity
    voice: nova  # Clear, articulate voice

skills:
  - screen-reader
  - email-reader
  - calendar-manager
  - web-browser

ai:
  instructions: |
    You are an accessibility assistant. Describe visual content clearly.
    Confirm actions before executing. Be patient and detailed.

Example interaction:

You: "Read my emails"
Assistant: "You have 3 unread emails. First email: From John Smith, subject 'Meeting Tomorrow', received 2 hours ago. Would you like me to read the full email?"

You: "Yes"
Assistant: [Reads email content]

You: "Reply saying I'll be there"
Assistant: "I'll send a reply saying 'I'll be there.' Shall I send it now?"

Use Case 3: Smart Home Control

Setup: Always-on voice assistant connected to Home Assistant

Configuration:

voice:
  enabled: true
  wake_word: "computer"

skills:
  - home-assistant-integration

integrations:
  home_assistant:
    url: http://homeassistant.local:8123
    token: your-ha-token

Example commands:

"Computer, turn on living room lights"
"Set bedroom temperature to 72 degrees"
"Lock all doors"
"What's the status of the front door?"
"Turn off all lights in 30 minutes"

See Home Assistant integration guide.

Use Case 4: Driving Assistant

Setup: Raspberry Pi in car with speaker, offline voice

Configuration:

voice:
  enabled: true
  stt:
    provider: whisper-local  # Works offline
  tts:
    provider: piper  # Works offline

ai:
  provider: ollama  # Local LLM for offline operation
  model: phi3

skills:
  - navigation
  - music-control
  - phone-calls
  - weather

Safety features:

• Completely hands-free operation
• No screen interaction required
• Works offline (no data usage)
• Quick, concise responses

Privacy Considerations

Maximum Privacy Configuration

For completely local, zero-cloud operation:

voice:
  wake_word:
    provider: snowboy  # Local wake word
    model_path: ./custom-wake.pmdl

  stt:
    provider: whisper-local  # Local speech recognition
    model: small

  tts:
    provider: piper  # Local text-to-speech
    model_path: ./voice-en-us-amy.onnx

ai:
  provider: ollama  # Local LLM
  model: llama3

logging:
  voice_recordings: false  # Don't save audio
  transcriptions: false  # Don't log text
  conversations: false  # Don't store messages

This configuration ensures: no internet connectivity required, no data sent to external servers, no conversation logging, and complete privacy for all interactions.

Partial Privacy (Cloud AI, Local Voice)

Balance privacy and AI quality:

voice:
  wake_word:
    provider: snowboy  # Local
  stt:
    provider: whisper-local  # Local
  tts:
    provider: piper  # Local

ai:
  provider: anthropic  # Cloud AI for quality
  model: claude-3-5-sonnet-20241022

privacy:
  strip_pii: true  # Remove names, emails, etc before sending to cloud
  anonymize_requests: true

Voice audio stays local—only anonymized text transcriptions sent to AI cloud.

Performance Optimization

Reduce Latency

1. Use fastest components:

voice:
  stt:
    provider: openai-whisper  # Faster than local for most

  tts:
    provider: openai  # Faster than ElevenLabs
    model: tts-1  # Not tts-1-hd

ai:
  model: claude-3-5-haiku-20241022  # Fastest model
  max_tokens: 100  # Shorter responses

2. Pre-load models (for local processing):

# Keep models in RAM
ollama pull llama3
ollama run llama3 &  # Keep running in background

3. Use GPU acceleration:

voice:
  stt:
    device: cuda  # For local Whisper

Optimize Audio Quality

1. Use good microphone:

• USB condenser mic ($30-80) much better than laptop built-in
• Reduce background noise (close windows, turn off fans)
• Optimal mic distance: 6-12 inches

2. Configure audio properly:

voice:
  audio:
    sample_rate: 16000  # Good balance
    noise_reduction: true
    automatic_gain: true

3. Test audio levels:

openclaw voice test-audio

# Adjust until waveform shows good signal without clipping

FAQ

Can I use custom wake words like “Jarvis” or “Computer”?

Yes, most wake word providers support custom words. Porcupine allows creating custom wake words at console.picovoice.ai (record yourself saying the word 3+ times). Snowboy requires training model at their site. Some pre-built options available: “Jarvis,” “Computer,” “Hey Siri,” “Alexa” (be careful of trademark issues if commercial use).

How much does it cost to run voice assistant monthly?

Costs vary by configuration. Completely local (Snowboy + local Whisper + Piper + Ollama): $0/month beyond electricity (~$2-5). Hybrid (Porcupine wake + OpenAI Whisper + OpenAI TTS + Claude): ~$10-30/month for moderate use (500-1500 interactions). Premium (ElevenLabs TTS + GPT-4): ~$50-150/month. Free tiers (Google Cloud STT/TTS) get you 60 minutes transcription and 1M characters TTS free monthly.

Does voice work offline?

Yes with proper configuration. Use local wake word (Snowboy), local STT (Whisper), local TTS (Piper), and local LLM (Ollama). Entire system runs without internet. Performance depends on hardware—Raspberry Pi 5 can handle lightweight setup, better computer recommended for good experience. Trade-off is lower voice quality vs cloud services.

Can I run voice assistant on Raspberry Pi?

Yes, Raspberry Pi 4/5 with 4-8GB RAM can run voice assistant. Use lightweight components (Snowboy wake word, small Whisper model, Piper TTS, Phi-3 Mini LLM). Performance adequate for personal use but not real-time conversations. Expect 2-5 second response latency. See our Raspberry Pi guide for detailed setup.

How accurate is speech recognition compared to Siri/Alexa?

OpenAI Whisper matches or exceeds commercial assistants for accuracy—often 95%+ word accuracy in quiet environments. Google Cloud Speech-to-Text also excellent (~90-95%). Quality factors: microphone quality (biggest factor), background noise, accent/dialect, internet speed (for cloud services). Local Whisper slightly less accurate than cloud but still very good with decent hardware.

Can voice assistant understand multiple languages?

Yes, configure multi-language support. Whisper supports 99 languages with auto-detection. Google Cloud Speech-to-Text supports 125 languages. Set language: auto for automatic detection or specify language code. For TTS, match response language to detected input language. Some languages have fewer voice options—English, Spanish, French, German, Chinese best supported.

How do I prevent false wake word activations?

Adjust sensitivity in config (lower = fewer false positives, but may miss intentional triggers). Choose distinctive wake words (3+ syllables, uncommon sounds). Train custom wake word with your voice specifically. Use push-to-talk instead of always-on wake word. Some solutions: two-stage activation (wake word + confirmation), visual indicator when listening (LED light), mute button for privacy.

Can I use voice assistant for dictation and transcription?

Yes, OpenClaw voice can transcribe meetings, notes, and dictation. Enable continuous listening mode, disable TTS (no voice responses), and configure for long-form transcription. Whisper excellent for this use case. Pro tip: Use openclaw voice transcribe --file meeting-audio.mp3 to transcribe pre-recorded audio files. Generate full transcripts with timestamps and speaker diarization (identifying different speakers).

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Next Steps

You now have all the knowledge to build a fully-functional hands-free AI voice assistant with OpenClaw.

To get started:

1. Install OpenClaw if you haven’t already
2. Follow this guide step-by-step to configure voice
3. Start with cloud services (easier), optimize for privacy later
4. Test extensively and refine audio settings for your environment

For related setups:

• Self-hosted AI for maximum privacy
• Local LLMs with Ollama
• Raspberry Pi deployment
• Top skills to enable

Join the community:

• Star OpenClaw on GitHub
• Share your voice assistant setup
• Contribute voice configurations and optimizations

Voice interfaces make AI more accessible, convenient, and natural. Build your personal voice assistant today and experience truly hands-free computing—on your terms.