Skip to main content

Agentic Speech Enhancement

Cleaner audio in, more accurate transcripts out.
Sanas Agentic Speech Enhancement (Agentic SE) models prepare audio for machines rather than human listeners. They isolate the primary speaker, strip background noise and distant voices, and hand a clean signal to your speech-to-text (ASR/STT) engine — reducing recognition errors and making downstream voice-agent behavior more reliable. All Agentic SE models are optimized for Human ↔ Machine experiences, where the “listener” is a transcription engine, IVR, or voice bot.

Why Agentic SE

  • Lower word error rates — cleaner input audio reduces Relative Word Error Rate (RWERR) for your ASR/STT.
  • Primary-speaker isolation — removes distant voices and background noise so the agent hears one speaker.
  • Multi-speaker awareness — a Standard variant preserves all human speech when background conversation carries context.
  • Telephony-ready — a variant tuned for 8kHz narrowband phone audio.

Core models

Agentic SE · Voice Isolation (General)

AGENTIC_VI_G_SE Removes background noise and distant voices for complete voice isolation of the primary speaker’s audio stream.

Agentic SE · Voice Isolation (Telephony)

AGENTIC_VI_GT_SE Telephony-optimized variant of Voice Isolation for 8kHz narrowband audio.

Agentic SE · Standard

AGENTIC_ST_SE Removes background noise while preserving all human speech for multi-speaker environments.

Model comparison

How to choose

  • Pick Voice Isolation — General (AGENTIC_VI_G_SE) when you want a single, isolated speaker stream for a voice agent, IVR, or phone bot at 16kHz.
  • Pick Voice Isolation — Telephony (AGENTIC_VI_GT_SE) for the same isolation on 8kHz narrowband telephony audio.
  • Pick Standard (AGENTIC_ST_SE) for multi-speaker environments where you need background noise removed but all human speech preserved.
Agentic SE models are designed to run before your ASR/STT engine to improve transcription accuracy. For audio intended for human listeners, use the Speech Enhancement (Human ↔ Human) models instead.