Lausanne: A Hub for Voice AI Innovation
Lausanne, home to EPFL and its research ecosystem in signal processing and AI, is a privileged location for exploring the architecture of intelligent voice systems. Voice AI — artificial intelligence applied to voice — is experiencing exponential growth, driven by advances in speech synthesis, voice recognition, and natural language understanding.
In 2025, Voice AI systems are no longer limited to consumer voice assistants (Alexa, Siri). They are penetrating the enterprise world: automated call centers, phone assistants, industrial voice control, accessibility and much more.
Reference Architecture of a Voice AI System
Complete Voice Pipeline
Audio Input (microphone/phone)
→ VAD (Voice Activity Detection)
→ STT (Speech-to-Text)
→ NLU (Natural Language Understanding)
→ Dialogue Manager / LLM
→ NLG (Natural Language Generation)
→ TTS (Text-to-Speech)
→ Audio Output (speaker/phone)
Each component of this pipeline represents a specific architectural challenge, and optimizing the whole determines the quality of the user experience.
Real-Time Constraints
Voice AI imposes extreme latency constraints:
| Component | Target Latency | Critical Threshold | |-----------|--------------|----------------| | VAD | < 50ms | 100ms | | STT | < 300ms | 500ms | | NLU/LLM | < 500ms | 1000ms | | TTS | < 200ms | 400ms | | Total pipeline | < 1s | 2s |
Beyond 2 seconds of total latency, the conversational experience degrades significantly. The user perceives an uncomfortable silence and loses trust in the system.
Speech-to-Text (STT): From Voice to Text
Modern STT Architectures
Whisper (OpenAI)
- Encoder-decoder transformer architecture
- Trained on 680,000 hours of multilingual audio
- State-of-the-art transcription quality
- Open-source, self-hosted deployment
- Supports 99 languages
Deepgram
- Proprietary architecture optimized for real-time
- Sub-300ms streaming latency
- Domain-specialized models (medical, finance, call center)
- SaaS API with volume pricing
Google Speech-to-Text v2
- USM (Universal Speech Model) based on foundation models
- Excellent multilingual and code-switching support
- Native GCP integration
STT Comparison
| Solution | Latency | Quality | Self-hosted | Price | |----------|---------|---------|-------------|------| | Whisper large-v3 | Medium | Excellent | Yes | Free | | Deepgram Nova-2 | Very low | Excellent | No | $0.0043/min | | Google STT v2 | Low | Very good | No | $0.006/min | | Azure Speech | Low | Very good | No | $0.005/min | | faster-whisper | Low | Excellent | Yes | Free |
STT Optimization
- Streaming: transcribe in real-time rather than waiting for the end of the sentence
- Endpointing: intelligently detecting the end of an utterance
- Custom vocabulary: adding industry-specific terms
- Noise reduction: audio pre-processing to improve quality
- Speaker diarization: identifying who is speaking in a conversation
Text-to-Speech (TTS): From Text to Voice
Evolution of TTS Architectures
TTS architecture has gone through three generations:
Generation 1: Concatenative
- Assembly of pre-recorded audio segments
- Limited quality, robotic voice
Generation 2: Neural TTS
- Tacotron, WaveNet, FastSpeech
- Natural voice but compute-intensive
Generation 3: Zero-Shot Voice Cloning
- XTTS, Bark, ElevenLabs
- Voice cloning from just a few seconds of audio
- Near-human quality
Production TTS Solutions
| Solution | Quality | Latency | Voice Cloning | Price | |----------|---------|---------|-------------|------| | ElevenLabs | Excellent | Low | Yes | $0.18/1K chars | | XTTS v2 | Very good | Medium | Yes | Free (open) | | Azure Neural TTS | Very good | Low | Yes (custom) | $0.016/1K chars | | Google Cloud TTS | Good | Low | No | $0.016/1K chars | | Cartesia Sonic | Excellent | Very low | Yes | Pay-per-use |
Streaming TTS
For a fluid conversational experience, TTS must work in streaming mode:
- The LLM generates text token by token
- TTS begins synthesis from the first words
- Audio is streamed to the client
- Result: the user hears the response almost instantly
The Vocalis platform masters these streaming techniques to deliver AI phone conversations with imperceptible latency.
NLU and Dialogue Management
Natural Language Understanding (NLU)
NLU transforms transcribed text into intent and entities:
- Intent detection: what does the user want to do? (book, cancel, inquire)
- Entity extraction: what specific elements? (date, location, amount)
- Sentiment analysis: what is the user's emotion?
- Context tracking: multi-turn conversational context tracking
LLM as Dialogue Manager
In 2025, LLMs are progressively replacing traditional NLU systems:
LLM Advantages:
- Superior contextual understanding
- No need to manually define intents
- Natural multi-turn conversation handling
- Reasoning and decision-making capabilities
LLM Dialogue Architecture:
STT Output (text)
→ System Prompt (role, instructions, constraints)
→ Conversation History (short-term memory)
→ Tool Definitions (available actions)
→ LLM (GPT-4, Claude, Llama)
→ Decision: text response OR tool call
→ TTS (if text response)
Telephony Architecture (SIP/VoIP)
Telephony Integration
For telephony use cases (call centers, automated switchboard), Voice AI architecture integrates with SIP/VoIP infrastructure:
Phone network (PSTN/SIP)
→ SIP Trunk Provider (Twilio, Telnyx, Vonage)
→ SIP Gateway → Media Server
→ Audio Stream → Voice AI Pipeline
→ Audio Response → Media Server → SIP
→ Back to caller
Telephony Components
| Component | Role | Options | |-----------|------|---------| | SIP Trunk | Phone connection | Twilio, Telnyx, Vonage | | Media Server | Audio processing | Asterisk, FreeSWITCH, Jambonz | | WebSocket | Bidirectional audio streaming | Custom, LiveKit | | DTMF Handler | Keypad management | Integrated in media server |
Call Management
A telephony Voice AI system must handle:
- Call transfer: to a human agent if needed
- Hold: hold music with periodic messages
- Conference: adding participants
- Recording: with consent, for quality and compliance
- DTMF: keypad interaction (menus, codes)
For an in-depth exploration of voice AI technologies, Vocalis Blog regularly publishes detailed technical analyses.
Multi-Modal Voice Architecture
Voice + Vision
The most advanced systems combine voice and vision:
- Smart displays: the voice assistant displays visual information
- Video call AI: visual analysis during a video call
- Ambient intelligence: the assistant understands the visual context
Voice + Agents
Integrating Voice AI with autonomous AI agents creates systems capable of:
- Understanding a complex voice request
- Planning and executing actions (booking, search, transaction)
- Communicating the result vocally
- Handling errors and requesting clarifications
Challenges and Solutions
Noise and Difficult Environments
- Noise cancellation: RNNoise, NVIDIA Maxine
- Beam forming: directional microphone focusing
- Acoustic Echo Cancellation: echo suppression in full-duplex
Multilingualism
- Language detection: automatic language identification
- Code-switching: handling mid-conversation language changes
- Accent adaptation: robustness to regional accents
In Switzerland, where four national languages coexist, these challenges are particularly acute. Voice AI systems deployed in Lausanne must handle French, German, Italian, and English fluently.
Accessibility
Voice AI is a major lever for accessibility:
- Voice interfaces for the visually impaired
- Voice control for people with reduced mobility
- Real-time subtitling for the hearing impaired
Voice AI Quality Metrics
| Metric | Description | Target | |----------|-------------|-------| | WER | Word Error Rate (STT) | < 5% | | MOS | Mean Opinion Score (TTS) | > 4.0/5 | | E2E Latency | Total pipeline time | < 1.5s | | Task Success Rate | Task completion rate | > 85% | | User Satisfaction | Satisfaction score | > 4.0/5 | | Containment Rate | Calls resolved without human | > 70% |
Enterprise Use Cases
Automated Call Center
The most commonly deployed use case: automating call handling for frequently asked questions, appointment scheduling, and intelligent routing. Discover real-world applications in our article on AI telephony.
Internal Voice Assistant
A voice assistant for employees: querying internal systems, dictating notes, automating workflows — all by voice.
Industrial Voice Control
In industrial environments (hands busy, noisy surroundings), voice control allows interacting with systems without a touchscreen.
Conclusion
Voice AI architecture is a fascinating field that combines signal processing, NLP, LLMs, and telephony infrastructure. The key to success lies in optimizing end-to-end latency and conversational experience quality.
Lausanne and French-speaking Switzerland are at the forefront of this innovation. To go further, explore our guide on AI chatbots for enterprises.
Also read: AI telephony and synthetic voice and our guide on AI architecture fundamentals. Also discover autonomous AI agent architecture and AI in Switzerland 2025.