What investigative steps would be required to authenticate the Substack audio recordings and the identity of the speaker?
Executive summary
Authenticating Substack audio and proving who is speaking requires a layered forensic campaign combining file-level forensics, signal analysis, and biometric speaker comparison, backed by reproducible lab work and documented chain-of-custody; no single test is decisive on its own [1][2]. Experts must balance technical tools — metadata, ENF, microphone fingerprinting, waveform continuity, MFCC/deep‑learning voice models — with legal standards, error-rate calibration, and disclosure of limitations such as recording device or channel mismatch [3][4][5].
1. Establish provenance and chain of custody before analysis
Begin by securing the earliest available copy and any original media, because forensic bit‑level copies preserve evidentiary value and allow verification of subsequent changes; analysts stress that original files or the earliest copies are required for valid testing and reporting [6][1].
2. Perform file‑system and metadata examination for red flags
Examine container metadata, timestamps, device fields and encoding history to detect evidence of editing software or recompression — metadata inconsistencies and signs of third‑party editing are common indicators of tampering, though metadata alone cannot prove forgery [7][1].
3. Run signal‑level integrity checks and waveform continuity tests
Inspect the waveform and spectrogram for discontinuities, abrupt phase shifts, or splice artifacts that suggest edits; organizations offering authentication services routinely use waveform continuity analysis and detection of recompression artifacts to identify manipulations [1][8].
4. Apply Electric Network Frequency (ENF) and environmental fingerprinting where possible
When the recording environment is connected to mains power, ENF traces embedded in recordings can be matched to grid logs to timestamp and validate continuity; however ENF is unusable for battery‑powered or isolated recordings, so its absence is not proof of tampering [3].
5. Identify the recording device and microphone signature
Extract device fingerprints and microphone characteristics using feature extraction (MFCCs and long‑/short‑term features) and compare to known device profiles; recent transformer and attention‑based microphone classification models can identify source microphones with high accuracy on curated databases [3][5].
6. Enhance audio carefully and document every transformation
Use forensic enhancement to improve intelligibility but log every processing step and work from an untouched copy; subtle boosts or cuts can obscure analysis and different listening outputs (headphones vs speakers) reveal different artifacts, so documentation is essential [9].
7. Conduct voice biometrics and calibrated speaker identification
Perform a formal speaker comparison using voice biometric systems (formant, pitch, spectral envelope, MFCC‑based modeling and statistical classifiers such as GMMs/HMMs/ANNs), calibrated to a stated False Acceptance Rate and trained on acoustically matched reference samples; speaker ID systems can be powerful but require domain‑matched calibration and substantial reference material to set error rates [2][4][10].
8. Use cross‑validation, independent experts, and disclosure of error rates
Have independent labs replicate results and disclose method limitations and calibration data; best practices call for peer review of methods and explicit statements of likelihood, not categorical identity claims — voice comparison should report match scores and calibrated FARs rather than absolute pronouncements [7][4].
9. Recreate the recording scenario to test hypotheses
Where possible, recreate the presumed recording setup (device, distance, codec, background noise) and compare signatures; Primeau and other labs advise creating test recordings with the claimed device and chain to evaluate whether the evidence is consistent with the claimed origin [7][6].
10. Prepare a defensible written report and preserve all artifacts
Produce a forensically defensible report that documents acquisition, tests, parameters, uncertainties and alternative explanations; courts require reproducible methods, preservation of original files, and expert disclosure of limitations such as poor-quality samples, channel mismatch or potential deepfake synthesis if suspected [1][11].
Alternative viewpoint and caveats: laboratories and vendors advertise high detection rates for tampering and speaker ID, but real‑world conditions — poor audio quality, limited reference samples, cross‑channel differences, intentional voice disguise or AI synthesis — reduce certainty and must be disclosed [5][12]. Some advanced microphone and transformer models show state‑of‑the‑art accuracy on benchmark datasets, yet those controlled datasets do not fully replicate messy user‑generated Substack uploads [3][5]. If original devices or log files are unavailable, authentication leans on probabilistic conclusions rather than absolute proof [6][1].