Hecttor logo

Human-to-Voice AI

Voice Isolation & Noise Cancellation Built for Conversations and AI Systems

Powered by Orpheus SDK, delivering voice isolation for speech recognition, AI voice agents, and downstream analytics systems.

Interface for Orpheus SDK showing real-time voice isolation for conversations and AI machine systems.

Low latency · Language agnostic · Easy integration

FROM RAW AUDIO TO CLEAN INPUT

Experience real-time voice isolation and structured output in action.

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

↓20–90% Relative WER Improvement

Measured improvement in transcription accuracy on noisy, multi-speaker audio.

<20ms Processing Latency

Real-time processing with no impact on conversation flow.

↑30% Turn Detection Accuracy

More precise end-of-speech detection for better response timing.

Cleaner Speech Segmentation

Precise VAD reduces false positives and missed speech in streaming pipelines.

FROM RAW AUDIO TO MACHINE-READY INPUT

From noisy audio to clean, structured input in real time.

Raw Audio Is Not Machine-Ready

Real-world audio is messy. Overlapping speakers, noise, and unclear boundaries break transcription and downstream systems.

  • Multiple speakers overlap and confuse ASR
  • Background noise corrupts transcription accuracy
  • No clear turn boundaries for agents to respond
  • Inconsistent input leads to unstable AI behavior

Your models are only as good as your audio.

Waveform graphic showing how overlapping speakers and background noise corrupt transcription and Voice AI behavior.

Isolate Speech. Remove Interference. Deliver Clean Input.

Orpheus SDK is a real-time audio processing engine that isolates the dominant voice and removes background noise, delivering clean, structured audio for both live conversations and AI systems.

  • Separates the main speaker from overlapping voices
  • Suppresses background noise and interference
  • Preserves speech clarity for accurate transcription
  • Works in real time across live conversations

Clean voice. Clear structure. Reliable output.

Visualization of the AI model isolating the primary speaker and delivering clean input for downstream AI models.

HOW VOICE ISOLATION WORKS

From raw, overlapping audio to clean, structured input ready for transcription, analytics, and voice AI.

Real-time by design

Processes audio instantly during live conversations, with no delay or post-processing.

Separates the dominant speaker

Identifies and isolates the primary voice, even in overlapping or multi-speaker scenarios.

Removes noise and interference

Suppresses background noise, artifacts, and competing signals that degrade clarity.

Delivers machine- ready input

Outputs clean, stable audio optimized for transcription, analytics, and voice AI systems.

BUILT AS A REAL-TIME AUDIO PROCESSING SYSTEM

Combines voice isolation, VAD, and turn detection into a single pre-ASR pipeline.

Turn Detection

Control turn-taking flow

Identifies when speech ends so systems respond at the right moment, without overlap or delay.

ASR Accuracy

CLEAN THE SIGNAL AT THE SOURCE

Separates the dominant speaker and removes competing voices and noise before processing begins.

Voice Activity Detection (VAD)

DEFINE WHEN SPEECH EXISTS

Filters silence and non-speech segments to stabilize streaming and downstream processing.

WHERE VOICE ISOLATION MATTERS MOST

Designed for environments where overlapping speech, noise, and unclear audio break transcription, analytics, and voice AI systems.

Icons for Voice AI agents, AI analytics performance, contact center operations, and communication infrastructure.

Voice AI and conversational agents

Ensure clean input for accurate understanding, stable responses, and natural interactions.

Icons for Voice AI agents, AI analytics performance, contact center operations, and communication infrastructure.

Improve AI performance

Cleaner audio leads to better transcription and more reliable analytics.

Icons for Voice AI agents, AI analytics performance, contact center operations, and communication infrastructure.

Contact centers and support operations

Reduce repetition and miscommunication in real-time conversations with customers.

Icons for Voice AI agents, AI analytics performance, contact center operations, and communication infrastructure.

Comms platforms and voice infrastructure

Deliver consistent audio quality across users, devices, and unpredictable environments.

Built for Machine Pipelines, Not Playback

Hecttor Orpheus SDK sits between raw audio and ASR, structuring speech before it reaches downstream systems.

  • Processes audio before transcription
  • Improves input quality for AI systems
  • No changes to existing architecture
  • Works in real-time streams
Unlike traditional noise cancellation built for listening, Hecttor is designed for machine understanding.
Technical flowchart showing Orpheus SDK as a pre-ASR layer structuring audio before it reaches the machine model.

FREQUENTLY ASKED QUESTIONS

What is voice isolation in audio processing?

Voice isolation separates the primary speaker from overlapping voices and background sounds, producing a clean signal that can be accurately processed by ASR and AI systems.

How is voice isolation different from noise cancellation?

Noise cancellation removes background sounds. Voice isolation goes further by separating the main speaker from other voices, making the audio usable for machine processing.

Can Hecttor handle multiple speakers on a call?

Yes. Hecttor isolates the dominant speaker and suppresses overlapping speech, improving clarity and transcription accuracy in multi-speaker environments.

How does voice isolation improve ASR accuracy?

By removing noise and separating speakers, Hecttor provides cleaner input to ASR systems, reducing word error rate (WER) and improving transcription consistency.

Fix the Input. Improve the Output.