This repository contains acoustic feature measurements extracted from the MLAAD (Multilingual Audio Anti-Spoofing Dataset) corpus by the Moonscape Synthetic Speech Atlas (SSA) pipeline.

298,000 rows. 152 columns. One row per MLAAD clip.

No audio files are included. Each row contains classical signal processing and biomechanical acoustic features extracted from the original MLAAD audio, plus Z-scores computed against the SSA biological baseline (VCTK anechoic chamber + AMI conversational speech).

The SSA pipeline is physics-grounded rather than learned — every feature measures a physical property of the vocal production system. This makes the measurements interpretable and directly comparable across datasets without retraining.

Files

File	Description
`mlaad_with_zscores_20260522.parquet`	298,000 rows × 152 columns. Raw acoustic features + Z-scores.
`docs/ssa_DATA_DICTIONARY.txt`	Full column-by-column reference with null counts and baseline parameters.
`docs/ssa_METHODOLOGY.txt`	SSA six-pass pipeline documentation and key findings.

Column Structure

Cols   1-9    Provenance metadata (data_provider, contact, distribution, etc.)
Cols  10-19   Identity and clip metadata (file_id, language, tts_system, tier, etc.)
Cols  20-93   Raw acoustic features (74 columns)
Cols  94-152  Z-score columns, Z_ prefix (59 columns)

Key Features

Primary SSA detection signals:

Column	Description	Typical spoof Z
`bico_f0_f1`	F0-F1 bicoherence coupling. Primary detection signal. TTS destroys the nonlinear coupling between glottal source and vocal tract filter.	−0.3 to −1.5
`spectral_tilt`	Log power spectrum slope. Only universally direction-consistent feature across all tested architectures.	+0.1 to +0.5
`modgd_var`	Modified Group Delay variance. Most robust under adversarial conditions.	−0.2 to −0.8
`nVIV`	Normalised Voiced Interval Variance. Rhythm metric. TTS collapses toward English stress-timed values regardless of target language.	varies by language
`iaif_residual_kurtosis`	Glottal residual kurtosis. Biological speech has leptokurtic GCI spikes; synthetic is near-Gaussian.	−0.2 to −0.5
`f0_declination_slope`	F0 trajectory slope. TTS has no lung pressure model — near-zero or artificially reset.	+0.3 to +1.0

Z-score interpretation:

|Z| > 3.0 → statistically significant physics anomaly
|Z| > 5.0 → high-confidence synthetic indicator
|Z| > 9.0 → definitive — observed in commercial TTS systems

Biological Baseline

Z-scores are computed against the SSA bifurcated VCTK/AMI baseline:

Baseline	Corpus	N	Use
`vctk_pool`	VCTK 0.92 anechoic (MKH800), pooled	88,326	Spectral, biomechanical, IAIF, voice quality features
`vctk_M/F`	VCTK, gender-stratified	M: 40,321 / F: 47,833	Formants, pitch features
`ami_M/F`	AMI Meeting Corpus, gender-stratified	M: 21,779 / F: 8,850	Macro-prosody (pause, F0 declination, nVIV)

Gender is inferred from pitch_mean via the SSA 165 Hz F0 pivot threshold. MLAAD contains no speaker gender labels.

Quick Load

import pandas as pd

df = pd.read_parquet('mlaad_with_zscores_20260522.parquet')

print(f"Rows: {len(df):,}  Columns: {len(df.columns)}")
print(f"Languages: {df['language'].nunique()}")
print(f"TTS systems: {df['tts_system'].nunique()}")

# Primary detection features by TTS system
df.groupby('tts_system')[['Z_bico_f0_f1', 'Z_spectral_tilt',
                           'Z_modgd_var', 'Z_nVIV']].mean().round(3)

# nVIV rhythm collapse by language
# Expected: tonal/syllable-timed languages ~30-45
# Observed: uniform collapse toward ~56 regardless of language
df.groupby('language')['nVIV'].agg(['mean', 'std', 'count']).sort_values('mean')

# High-quality clips only (Tier 1 = PRISTINE conditions)
tier1 = df[df['tier'] == '1']

# IAIF glottal features (most meaningful in Tier 1)
tier1[['tts_system', 'Z_iaif_residual_kurtosis',
       'Z_iaif_gci_regularity', 'Z_iaif_hf_energy_ratio']].groupby('tts_system').mean()

Known Nulls

Column(s)	Null count	Reason
All raw features	119	Clips with extraction failures at ingestion
`spectral_aliasing_ratio`, `Z_spectral_aliasing_ratio`	298,000 (all)	MLAAD is 16 kHz audio; 12-16 kHz band is above Nyquist ceiling. Expected and correct.
`jitter_local`, `shimmer_local`, `hnr_mean`, `cpps`	varies	PRISTINE gate — suppressed for T3/T4 clips (SNR < 50 dB or C50 < 50 dB)
`fam_75hz_sharpness`, `fam_86hz_sharpness`, `inertial_decay_residual`	3,945	Extraction threshold-dependent
`transcript`, `phonemes`, `vot_candidates`	119 / 298,000	Empty scaffolding columns from ingestion pipeline

Pipeline Summary

The SSA applies six sequential extraction passes to each clip:

Pass	Tool	Features
P1	Brouhaha (Lavechin et al. 2022)	SNR, C50, speech ratio, quality tier
P2	Parselmouth/Praat, librosa, scipy	54 classical features
P3	IAIF (Alku 1992), librosa LPC	7 glottal residual features
P4	Parselmouth + scipy VAD	6 macro-prosody features
P5	scipy, Parselmouth	4 kitchen sink features
P6	numpy/scipy (pure FFT)	6 forensic features

Full pipeline documentation: docs/ssa_METHODOLOGY.txt

Key Finding: nVIV Rhythm Collapse

Across 100+ languages in MLAAD, TTS systems collapse nVIV (rhythm variability) toward English stress-timed values (~55-65) regardless of the target language's typological class. Tonal languages (Mandarin, Thai, Yoruba) and syllable-timed languages (French, Spanish, Italian) would be expected to show nVIV ~30-45. Observed across MLAAD: ~56.75 uniformly.

This is a generational finding — modern large-scale systems show improvement, particularly in languages well-represented in training data. Use Z_nVIV as a routing gate for language-typology analysis rather than a binary classifier.

Distribution

NON-COMMERCIAL — NOT FOR PUBLIC DISTRIBUTION

This file is provided as a research audit contribution to the MLAAD dataset team. It is not a general public release.

Contact: chris@moonscapesoftware.ca

Citation

If you use these measurements in your research, please cite:

SSA pipeline:

Moonscape Software (2026). Synthetic Speech Atlas: Physics-Grounded Acoustic
Feature Extraction for Speech Deepfake Detection. SSA_MLAAD_v1 audit export.
Contact: chris@moonscapesoftware.ca

MLAAD dataset:

Müller, N. et al. (2024). MLAAD: The Multilingual Audio Anti-Spoofing Dataset.
Proceedings of Interspeech 2024.

Brouhaha:

Lavechin, M. et al. (2022). Brouhaha: Mono-channel speech assessment toolkit.
Interspeech 2022.

Moonscape Software | Synthetic Speech Atlas | SSA_MLAAD_v1 chris@moonscapesoftware.ca

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