@svpino: Cutting down noise before sending the audio to a speech-to-text model makes a huge improvement. Voice isolation is the …
Summary
Krisp released an open benchmark and dataset showing that voice isolation reduces word error rates in speech-to-text models by 73%, with significant improvements across workplace and call-center recordings.
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Cutting down noise before sending the audio to a speech-to-text model makes a huge improvement.
Voice isolation is the next big thing you want to pay attention to.
Krisp just released an open benchmark and a dataset to measure the impact of voice isolation on STT accuracy.
The results are eye-watering:
73% reduction in WER across 265 real recordings and 11 speech-to-text configurations.
This compares an original recording with a voice-isolated version.
Here are the results with voice isolation:
• Overall word error rate: Down from 23.24% to 6.24%. • Workplace recordings: Down from 31.84% to 6.30%. • Call-center recordings: Down from 23.83% to 6.86%.
If you have a speech-to-text setup, you can reproduce these experiments using your audio and see whether voice isolation helps.
Here is the link: https://partner.krisp.ai/svpino-x-bm
Thanks to the Krisp team for partnering with me on this post.
Voice Isolation STT Benchmark | Krisp
Source: https://krisp.ai/benchmarks/voice-isolation-benchmark/?utm_source=svpino&utm_medium=twitter&utm_campaign=vi_benchmark_2026&utm_content=benchmark This page requires JavaScript to display.
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