---
title: "Audio data QA and acceptance criteria for AI | YPAI"
url: https://ypai.ai/compliance/audio-data-qa/
description: "Define measurable acceptance criteria, review coverage, exception handling, and evidence packages for audio data quality assurance."
source: "src/copy/routes (route /compliance/audio-data-qa/)"
---

# Audio data QA and acceptance criteria for AI

> Define measurable acceptance criteria, review coverage, exception handling, and evidence packages for audio data quality assurance.

- Where audio data problems surface late

## Transcription accuracy

- Word Error Rate measured against an agreed reference set

## Speaker diarisation

- Speaker boundaries reviewed against agreed references

## PII redaction

- Method, coverage, and escalation rules agreed before delivery

## Acoustic quality

## Metadata validation

- 16 kHz / 16-bit standard, timestamp accuracy

## Edge case handling

- Custom lexicons, annotators who speak the dialect or know the domain

## Ingestion

- Automated format validation, sample rate checks, clipping detection

## Automated QA

- SNR analysis, silence detection, PII scan, metadata validation

## Human review

## Expert adjudication

- Edge case resolution, domain terminology validation

## Delivery

- Acceptance gate, agreed evidence package, QA report

## Consent evidence

- Collection evidence where the project scope requires it

## Protocol summary

- Version-controlled acceptance criteria and QA methodology

## QA report

- Quality results at the coverage level defined in the protocol

## Exception log

- Documented handling of edge cases and rejections

## What audio formats and codecs do you support?

We accept WAV, FLAC, MP3, and most common codecs. Standard delivery is uncompressed 16 kHz / 16-bit WAV, or your specified format. Transcoding handled as part of ingestion.

## Can I define custom acceptance criteria?

Yes. We co-design acceptance criteria with your team before project kickoff. This covers WER thresholds, acoustic quality requirements, metadata specifications, and domain-specific rules.

## What happens if data fails acceptance criteria?

The remediation path is agreed in the project protocol. Depending on the failure type, this can include correction, re-collection, exclusion, or documented acceptance of an exception.

## How is PII redaction checked?

Detection methods, human review coverage, evidence fields, and escalation rules are defined for the data and risk profile. Any rights-request process is documented in the applicable project agreement.

## What documentation do you provide for ML audits?

The evidence package can include a dataset card, collection and annotation guidance, agreed metadata, QA results, an exception log, and version history. The exact artefacts are fixed during scoping.

## What is the typical QA turnaround time?

Timing depends on volume, review coverage, language mix, acoustic conditions, and the acceptance gate. The delivery plan is agreed after the source data and criteria are reviewed.

## How do you handle edge cases like accents or domain terms?

We build custom lexicons for your domain and recruit annotators who speak the dialect or know the domain when required. Edge cases are escalated to expert adjudicators with documented resolution.