Real-Time Speech Understanding for Voice Agents
Multi-Hypothesis ASR with Contextual Error Correction
Traditional ASR systems trust the single best hypothesis, missing opportunities to correct errors.
Hypothesis Rescoring
10-20
Multi-hypothesis
Languages Supported
25+
Multilingual
Executive Summary
What we built
An ASR pipeline combining Deepgram Nova-2 with multi-hypothesis error correction and contextual rescoring — achieving robust transcription even in noisy, multilingual environments.
Why it matters
Traditional ASR systems trust the single best hypothesis, missing opportunities to correct errors. In voice agent contexts, ASR errors cascade through the entire pipeline: ASR Error → Wrong Intent → Wrong Response → Bad Experience.
Results
- 10-20 hypothesis rescoring for improved accuracy
- <200ms processing latency for real-time use
- 50% fewer ASR errors with noise filtering
- >95% intent recognition accuracy
Best for
- →Real-time voice agent deployments
- →Healthcare with medical vocabulary
- →Multilingual customer support
- →Noisy call center environments
Limitations
- Domain-specific vocabulary requires tuning
- Medical vocabulary currently English/Spanish only
- Performance depends on audio quality
How It Works
A two-layer detection system where each covers the other's weaknesses.
Primary ASR Engine
Deepgram Nova-2 for real-time transcription
- Stream audio to Deepgram Nova-2
- Return N-best hypotheses with confidence scores
- Support 25+ languages with auto-detection
Multi-Hypothesis Correction
Contextual rescoring to select best interpretation
- Analyze 10-20 hypotheses per utterance
- Apply conversational context weighting
- Score by task coherence and domain vocabulary
Uncertainty Handling
Human-like clarification when confidence is low
- Detect low acoustic confidence
- Identify critical information (dates, names, numbers)
- Prompt for clarification when needed
Product Features
Ready for production with enterprise-grade reliability.
Multi-Hypothesis Rescoring
Instead of trusting 1-best output, analyze 10-20 hypotheses using conversational context and task coherence.
25+ Language Support
Global deployment ready with automatic language detection and mid-conversation switching.
Human-Like Clarification
When confidence is low, ask for clarification just like a human would — better than guessing wrong.
Domain-Specific Tuning
Medical terminology, drug names, financial terms — vocabulary models for specific industries.
Noise Robustness
50% fewer ASR errors with integrated noise filtering. Works in call centers and speakerphone scenarios.
<200ms Latency
Real-time processing with <50ms additional latency for multi-hypothesis rescoring.
Integration Details
Runs On
Cloud (Deepgram API) + Edge processing
Latency Budget
<200ms end-to-end
Providers
Deepgram Nova-2, Custom domain models
Implementation
1-2 days for standard, 1-2 weeks for domain tuning
Frequently Asked Questions
Common questions about our voicemail detection system.
Ready to see this in action?
Book a technical walkthrough with our team to see how this research applies to your use case.