Emergency Audio Privacy: Modern De-identification Standards

The Privacy Challenge

Critical Priority

Emergency response systems generate terabytes of audio data daily. While crucial for training AI dispatchers, this data contains Personally Identifiable Information (PII) and biometric voiceprints that can re-identify speakers. The VoicePrivacy Challenge (2020–2024, results published Jan 2025) is the primary benchmark, with its 2024 edition attracting 36 systems from 13 teams across 16 countries evaluated against 6 baselines (B1–B6). ICASSP 2025 introduced the VoicePrivacy Attacker Challenge, focusing on breaking anonymization systems.

Data Disclaimer

Metrics are derived from published VPC 2024 results (Jan 2025), ICASSP 2025, and peer-reviewed research. PII distribution is illustrative. EER/WER/UAR figures reflect VPC baselines (B1–B6) and best submitted systems. Exact values vary by deployment.

Best EER (Semi-informed)

22–34%

VPC 2024 B5 baseline (50% = random)

Manual Redaction Speed

3–5x RT

Hours of review per hour of audio

ASR WER (post-anon)

4–5%

VPC 2024 B5 (lower = better)

Emotion UAR (original)

~64%

IEMOCAP 4-class SER baseline

PII Entity Distribution

Illustrative breakdown of sensitive entity types in emergency audio. Actual ratios vary by call type and jurisdiction.

Processing Scalability

Estimated throughput: Human analyst (3–5x real-time) vs. GPU-accelerated pipeline (~0.1x real-time).

The Privacy Challenge

Data Disclaimer

PII Entity Distribution

Processing Scalability

Report Sections

Key Insight