Voice Interface Layer
Real-time voice capabilities for your AI agents and customer-facing systems. From low-latency speech recognition to custom voice synthesis deployed on-premise with no audio data leaving your environment.
Low-Latency Speech Recognition
Real-time ASR with minimal transcription latency using on-premise Whisper or similar deployments. Optimised for streaming audio — call centre calls, live meetings, or real-time agent interactions.
Custom Text-to-Speech
Branded voice synthesis that matches your communication style. Custom TTS models can be trained on your existing audio assets to produce a consistent, professional voice — not generic robotic output.
Domain-Specific Vocabulary
Technical terminology, product names, and industry jargon are handled correctly. Custom language models and pronunciation dictionaries ensure that specialised vocabulary is recognised and spoken accurately.
Multi-Language Voice
Voice interfaces for multilingual environments. ASR and TTS models fine-tuned for local languages and accents — not just translation overlays. Customer-facing voice AI that sounds natural to native speakers.
Why On-Premise Voice AI?
Voice data is among the most sensitive information an organisation handles. Customer phone calls, internal meetings, patient consultations, and financial discussions all contain information that should not be transmitted to external cloud services for transcription or synthesis. Cloud ASR providers process your audio on their infrastructure, often retaining recordings for model improvement purposes — a compliance liability for organisations subject to HIPAA, GDPR, or financial data protection regulations.
On-premise voice processing ensures that all audio data — both the raw recordings and the resulting transcriptions — stays within your network. There is no dependency on external API availability, no per-minute transcription costs at scale, and no risk of audio data being used to train third-party models. Your voice data remains yours.
Use Cases
- —Contact centre automation: Real-time transcription of customer calls with simultaneous intent classification. Agents receive live suggestions and knowledge base lookups based on what the customer is saying — reducing handle time and improving first-call resolution rates.
- —Voice-driven AI agents: Conversational AI that customers or employees interact with by speaking naturally. The voice interface handles speech-to-text, passes the text to an AI agent for processing, and returns a spoken response — all with sub-second latency on properly provisioned hardware.
- —Meeting transcription and summarisation: Live transcription of internal meetings with automated action item extraction and summary generation. Transcripts are stored on your infrastructure and searchable through your knowledge management system.
- —Clinical documentation: Physicians dictate clinical notes which are transcribed and structured into the appropriate EHR fields in real time. Domain-specific vocabulary models ensure medical terminology is transcribed accurately without manual correction.
- —Accessibility: Voice interfaces for applications that need to be accessible to users who cannot interact via keyboard or screen. Real-time TTS for screen readers, voice navigation for enterprise applications, and spoken alerts and notifications.
Architecture
A typical voice interface deployment consists of three interconnected layers:
Audio capture and streaming
Audio is captured via WebRTC, SIP trunk, or direct microphone input and streamed in real-time to the ASR service. We handle codec negotiation, echo cancellation, noise suppression, and automatic gain control to ensure clean input regardless of the audio source quality.
Speech recognition (ASR)
Whisper or Wav2Vec 2.0 models process the audio stream and produce text transcriptions. For streaming use cases, we deploy chunked inference with partial result emission — users see transcription appearing in real-time rather than waiting for the full utterance. Custom vocabulary and pronunciation dictionaries are loaded at model startup.
Speech synthesis (TTS)
When the AI agent generates a text response, the TTS service converts it to natural-sounding speech using Coqui TTS or NVIDIA Riva. Custom voice models can be trained on your brand audio assets. Output is streamed back to the client with latency under 300ms on GPU-equipped servers.
Performance and Hardware
Voice AI has strict latency requirements — users expect near-instant response. On properly provisioned hardware (NVIDIA A10G or equivalent with 24GB VRAM), Whisper Large V3 processes audio at approximately 10x real-time speed, meaning a 10-second audio clip is transcribed in about 1 second. For streaming ASR, we use the Whisper medium or small models which achieve 30x+ real-time speed with minimal accuracy degradation on English and major European languages. TTS synthesis adds approximately 200 to 300ms of latency for typical response lengths. Total round-trip time from user utterance to spoken AI response is typically under 2 seconds in a well-provisioned deployment.
Technology Stack
ASR, TTS, and streaming infrastructure