↑ Voice Activity Detection Accuracy
More accurate speech detection reduces missed and false activations.
SEE ORPHEUS SDK IN ACTION
Experience how raw audio is transformed into structured, machine-ready input in real time.
Main speaker
.......HiIlike...thoseschedulestomorrowmorning.........................MyconfirmationnumberisAX402..and......canyoualsoswitchmetoanhonesty..andCindyupdatedeachothertomymeal.Thankyou.
Background:
.......shhh,don'tcryLili,don'tcry,.......mommy'sgonefor5min........She'llbebacksoon........shhh,don'tcry............................
Works across all languages
MEASURED IMPACT ON MACHINE PERFORMANCE
↓50% Fewer False Speech Triggers
Filters out noise and silence that would otherwise trigger the system.
More Stable Audio Streams
Consistent speech boundaries stabilize streaming and downstream systems.
Reduced Unnecessary Processing
Processes only real speech, reducing compute and improving efficiency.
FROM CONTINUOUS AUDIO TO STRUCTURED SIGNAL
Real-time systems break when they can't distinguish speech from silence or noise.
Speech Boundaries Are Unclear in Real Time
Systems struggle to detect when speech starts and ends, leading to false triggers, missed input, and unstable processing.
- Silence and noise trigger false activations
- Speech segments are missed or cut off
- Continuous audio streams lack clear boundaries
- Unstable input breaks downstream systems
Your models are only as good as your input boundaries
Detect Speech. Define Boundaries. Stabilize the Input.
Hecttor detects voice activity in real time, separating speech from silence and noise to deliver clean, structured input for ASR and AI systems.
- Accurately detects speech start and end
- Filters silence and non-speech in real time
- Reduces false activations and missed segments
- Stabilizes streaming input for downstream systems
Clear boundaries. Stable input. Reliable systems.
HOW VOICE ACTIVITY DETECTION WORKS
From continuous audio streams to precise voice signals that trigger transcription, analytics, and real-time AI responses.
Real-time by design
Continuously analyzes incoming audio streams to detect voice activity instantly, with no delay or buffering.
Detects when speech starts and stops
Identifies the exact moments speech begins and ends, even in noisy or unpredictable environments.
Filters out silence and background noise
Ignores non-speech input, reducing false triggers and unnecessary processing.
Triggers downstream systems accurately
Ensures transcription, analytics, and voice agents activate only when meaningful speech is present.
A REAL-TIME SYSTEM FOR MACHINE-READY AUDIO
Three layers working together before ASR to produce clean, structured input.
WHERE VOICE ACTIVITY DETECTION MATTERS MOST
Designed for environments where systems must detect exactly when speech starts and stops to trigger transcription, analytics, and real-time responses.
Voice AI and real-time agents
Ensure systems listen and respond only when speech is present, improving timing and interaction accuracy.
Analytics and transcription systems
Capture only relevant speech, reducing noise, false triggers, and unnecessary processing.
Contact centers and support operations
Prevent interruptions and delays by accurately detecting when customers start and stop speaking.
Comms platforms and voice infrastructure
Enable efficient processing at scale by activating systems only when meaningful speech is detected.
Built for Machine Pipelines, Not Listening
Hecttor Orpheus SDK sits between raw audio and ASR, structuring speech before it reaches downstream systems.
- Processes audio before transcription
- Delivers structured input for ASR and AI systems
- Defines clear speech boundaries in real time
- Requires no changes to your architecture
FREQUENTLY ASKED QUESTIONS
What is Voice Activity Detection (VAD)?
VAD detects when speech is present in an audio stream. It separates speech from silence and noise in real time, enabling systems to process only relevant audio.
Why is VAD important for voice AI systems?
Without VAD, systems process continuous audio blindly. This leads to false activations, missed speech, and unstable performance. VAD ensures clean, structured input.
Does VAD improve ASR accuracy?
Yes. By filtering non-speech and defining clear speech segments, VAD reduces errors and improves transcription quality, especially in real-time scenarios.
How is VAD different from noise cancellation or voice isolation?
Noise cancellation removes background noise. Voice isolation separates speakers. VAD determines whether speech exists at all. Each solves a different problem in the pipeline.