vadette/macro_prosody_sample_set

Alexandria Voice Corpus — Korean & Hindi Macro-Prosody Telemetry

A sample release from the Alexandria Voice Corpus, a multilingual acoustic telemetry database spanning 60+ languages. This pack contains macro-prosodic feature extractions for Korean (6,998 clips) and Hindi (18,447 clips), derived from the Mozilla Common Voice CV24 corpus (CC0).

No audio is included. This is a structured feature dataset for linguistic research, speech technology development, and cross-linguistic prosody analysis.

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Dataset Details

What is macro-prosody telemetry?

Macro-prosody refers to the suprasegmental properties of speech — pitch contour, rhythm, intensity, and voice quality — measured at the clip level rather than the phoneme level. Each row in this dataset represents one spoken utterance with 20+ acoustic features extracted from it.

This is distinct from transcription, alignment, or phoneme-level data. It is designed for population-level acoustic analysis, language typology research, and training prosody-aware speech models.

Dataset Sources

• Source project: Mozilla Common Voice
• Source license: CC0-1.0
• Part of: Alexandria Voice Corpus (Orator Forge)

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Uses

Direct Use

• Cross-linguistic prosody comparison between Korean (language isolate) and Hindi (Indo-Aryan)
• Training or evaluating prosody-aware TTS and ASR models
• Rhythm typology research (e.g. mora-timed vs stress-timed speech)
• Voice quality and breathiness studies
• Speaker demographic modeling from acoustic features (population level)
• Feature engineering for downstream speech classification tasks

Out-of-Scope Use

• Speaker identification or re-identification — this dataset has been deliberately anonymized to prevent linking acoustic features back to individual speakers. Any attempt to do so violates the terms of use.
• Direct audio reconstruction — no audio is present in this dataset.
• Tasks requiring phoneme-level or word-level timing — use a force-aligned corpus instead.

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Dataset Structure

Each parquet file contains one row per utterance. Files are Snappy-compressed.

Column	Type	Description
clip_id	string	Anonymized sequential ID (e.g. korean_cv24_004521)
lang	string	BCP-47 language code
lang_name	string	Language name
quality_tier	int	1 (best) – 2 (good). Only T1/T2 clips included
duration_ms	int	Clip duration, bucketed to nearest 100ms
gender	string	male / female / unknown
gender_source	string	meta (self-reported) / inferred (pitch-based) / unknown
age	string	Age bracket (CV metadata where available)
syllable_count_approx	int	Approximate syllable count (vowel-count proxy)
pitch_mean	float32	Mean F0 in Hz
pitch_std	float32	F0 standard deviation
pitch_range	float32	F0 range (max – min) in Hz
pitch_velocity_max	float32	Max rate of F0 change (Hz/s)
intensity_mean	float32	Mean RMS intensity (dB)
intensity_max	float32	Peak intensity (dB)
intensity_range	float32	Intensity dynamic range (dB)
hnr_mean	float32	Harmonics-to-noise ratio (dB)
cpps	float32	Cepstral peak prominence smoothed — breathiness indicator
jitter_local	float32	Cycle-to-cycle pitch perturbation
shimmer_local	float32	Cycle-to-cycle amplitude perturbation
spectral_centroid_mean	float32	Mean spectral centroid (Hz)
spectral_tilt	float32	Spectral slope (relates to voice effort)
mfcc_delta_mean	float32	Mean MFCC delta (rate of spectral change)
zcr_mean	float32	Zero-crossing rate
teo_mean	float32	Teager energy operator mean
npvi	float32	Normalized pairwise variability index (rhythm metric)
articulation_rate	float32	Syllables per second (speech only)
speaking_rate	float32	Syllables per second (total duration)
pause_rate	float32	Pauses per second
speech_ratio	float32	Proportion of clip containing voiced speech
snr_median	float32	Signal-to-noise ratio, median (Brouhaha)
c50_median	float32	C50 clarity metric, median (Brouhaha)
f1_mean	float32	First formant mean (Hz) — note: may be 0.0 in this release
f2_mean	float32	Second formant mean (Hz) — note: may be 0.0 in this release
f3_mean	float32	Third formant mean (Hz) — note: may be 0.0 in this release

Quality Tiers

Clips were graded using Brouhaha (SNR + C50 + VAD scoring):

Tier	SNR	C50	Speech ratio	Description
T1	≥ 20 dB	≥ 20 dB	≥ 0.6	Studio quality
T2	≥ 10 dB	≥ 5 dB	≥ 0.4	Clean field recording

Only T1 and T2 clips are included in this release.

Files

korean_cv24.parquet     — 6,998 rows
hindi_cv24.parquet      — 18,447 rows

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Dataset Creation

Curation Rationale

There is a significant gap in publicly available acoustic feature datasets for non-Western and non-European languages. Korean and Hindi together represent over 600 million speakers across two typologically distinct language families — a language isolate and an Indo-Aryan branch of Indo-European. This release provides a free, CC0-licensed baseline for researchers who need structured prosodic features without needing to process raw audio.

Source Data

Data Collection and Processing

Source audio was drawn from Mozilla Common Voice CV24, a crowd-sourced corpus of read speech recorded by volunteers under a CC0 license.

Processing pipeline:

1. MP3 source audio converted to 16kHz mono WAV (ffmpeg, -20 dBFS normalization)
2. Quality grading via Brouhaha (SNR, C50, VAD) — only T1/T2 retained
3. Acoustic feature extraction via Parselmouth/Praat at 16kHz
4. Anonymization and precision degradation applied at export (see below)

Source Data Producers

Recordings were made by volunteer contributors to the Mozilla Common Voice project. Contributors self-reported demographic metadata (age, gender, accent) where willing.

Anonymization

This dataset applies the orator_forge_k5_v1 anonymization standard:

• Original Mozilla filenames replaced with sequential anonymized clip IDs
• Transcripts removed entirely (approximate syllable count provided as proxy)
• All continuous acoustic variables truncated to 2 decimal places and stored as float32
• Duration bucketed to nearest 100ms to prevent cross-referencing with source audio
• K-anonymity suppression at k=5: rows where the combination of {gender, age_bucket, duration_bucket} has fewer than 5 members are excluded

Personal and Sensitive Information

• No names, speaker IDs, or any directly identifying information is present
• No original audio is included
• Demographic fields (age, gender) are self-reported by Mozilla Common Voice contributors and are optional — many rows will show unknown
• Formant data (F1/F2/F3) is present but returns 0.0 in this release due to a known extraction issue; this will be corrected in v1.1

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Bias, Risks, and Limitations

• Gender balance: Gender is inferred from pitch for clips lacking self-reported metadata. Pitch-based inference has known limitations for speakers with atypical voices, tonal language speakers, and non-binary individuals. The gender_source field distinguishes self-reported from inferred labels.
• Recording conditions: Common Voice is read speech recorded in uncontrolled environments. Acoustic conditions vary significantly across contributors.
• Age distribution: CV contributor demographics skew younger and technically literate. This dataset is not a representative sample of the full speaker population of either language.
• Hindi script diversity: Hindi CV24 clips include speakers from a wide range of regional backgrounds with varying accent profiles. No regional stratification has been applied in this release.
• Formant zeros: F1/F2/F3 return 0.0 across all clips in this release. Do not use formant columns until v1.1.
• Prohibited use: Do not use this dataset to attempt speaker identification or re-linking to source audio. This violates the terms of use regardless of technical feasibility.

Recommendations

Use the gender_source field to filter to self-reported gender labels if demographic accuracy is important for your use case. For cross-linguistic rhythm comparisons, nPVI and articulation rate are the most reliable features in this release. Formant-dependent analyses should wait for v1.1.

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Citation

If you use this dataset, please cite the Mozilla Common Voice project as the source corpus:

BibTeX:

@dataset{alexandria_korean_hindi_prosody_2026,
  title     = {Alexandria Voice Corpus — Korean \& Hindi Macro-Prosody Telemetry},
  author    = {Orator Forge},
  year      = {2026},
  license   = {CC0-1.0},
  note      = {Derived from Mozilla Common Voice CV24 (CC0). 
               Acoustic features extracted via Parselmouth/Praat.}
}

@misc{mozilla_common_voice,
  title     = {Common Voice: A Massively-Multilingual Speech Corpus},
  author    = {Ardila, Rosana and others},
  year      = {2020},
  url       = {https://commonvoice.mozilla.org}
}

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Glossary

Term	Definition
F0 / pitch_mean	Fundamental frequency — the perceived pitch of the voice, measured in Hz
HNR	Harmonics-to-noise ratio — higher values indicate cleaner, more tonal voice quality
CPPS	Cepstral peak prominence smoothed — lower values indicate breathier voice
nPVI	Normalized pairwise variability index — measures durational variability between adjacent syllables; higher in stress-timed languages
C50	Clarity metric from room acoustics; higher = less reverb/echo in the recording
SNR	Signal-to-noise ratio — higher = cleaner recording
Brouhaha	Quality scoring model used for grading: github.com/marianne-m/brouhaha-vad
T1/T2	Quality tiers assigned by Brouhaha grading (see Dataset Structure)
orator_forge_k5_v1	Anonymization standard: k=5 suppression + sequential IDs + 2dp truncation + 100ms duration bucketing

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Dataset Card Contact

c.kleingertner@gmail.com ``