Why Background Noise Breaks Speech-to-Text Accuracy (and How to Fix It)

Arctan Eigen — TL;DR: Background noise, echo, clipping, and competing speech can obscure or distort the words a speech-to-text (STT) system receives. Identify the defect in the actual audio first. Improve capture and configuration, then test Eigen or another enhancement against the original stream with the same recognizer; preprocessing is not a guaranteed improvement.

Noise is only one possible cause of a bad transcript. A wrong language setting, mismatched sample rate, unfamiliar product name, or low-quality codec can produce similar symptoms. Start with the input sent to STT, not a noise-reduction setting. Google’s STT best practices call out microphone placement, encoding, clipping, and request configuration as factors to check.

Which errors suggest an audio problem?

Listen to a sample of calls before changing the model. Common patterns include quiet syllables disappearing under steady noise, an echo of the agent being transcribed as the caller, or a nearby person’s words entering the transcript. These are diagnostic clues, not proof of a specific cause. A background speaker can also trigger a false turn or interrupt the agent; LiveKit notes that unwanted audio can interfere with both transcription and turn detection.

Split your sample by environment: quiet calls, stationary noise, intermittent noise, overlapping speakers, and poor network audio. If errors appear just as often in quiet calls, check the STT configuration and vocabulary before treating noise cancellation as the solution.

Should you remove noise before speech-to-text?

Sometimes. A suitable processor may make the intended speaker clearer, and published speech-enhancement research reports better recognition in the tested noisy-speech setup. That result does not transfer automatically to every recognizer or call type. Enhancement can also remove weak speech cues or introduce artifacts. Google’s own guidance says its recognizer is designed for noisy audio and warns that noise-reduction preprocessing can lower accuracy for that service.

This makes the choice empirical. Compare three paths when possible: the existing input, improved capture or configuration, and input processed with Eigen. Keep the same STT model and decoding settings for each comparison. Avoid stacking two enhanced filters without a separate test; LiveKit warns about feeding already processed audio into another cancellation model.

Which measurements matter beyond a cleaner-sounding clip?

Start with a checked reference transcript. Word error rate (WER) counts substitutions, deletions, and insertions relative to the number of words in that reference, as described by NIST’s ASR metrics. Calculate WER for the full set and for each noise cohort; a single average can hide regressions on quiet or soft-spoken calls.

Then score the task itself:

Use the same consented recordings across variants, review errors manually, and include both quiet and noisy conditions. If the processed path wins only for some cohorts, apply it selectively where your platform supports that decision.

What should you do next?

Fix capture or configuration defects first. If competing human speech is the main issue, test Eigen’s voice isolation rather than assuming background suppression alone will solve it; the comparison explains the distinction. For a Python voice agent, use the integration checklist to add a bypass and measure the result.

Written by the Arctan team.