Datasets:
license: cc0-1.0
task_categories:
- feature-extraction
language:
- ko
- hi
tags:
- audio
- speech
- prosody
- acoustics
- linguistics
- phonetics
- voice-analytics
pretty_name: Alexandria Voice Corpus — Korean & Hindi Macro-Prosody Telemetry
size_categories:
- 10K<n<100K
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.
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 Description
- Curated by: Orator Forge
- Language(s): Korean (
ko), Hindi (hi) - Source corpus: Mozilla Common Voice CV24 (CC0-1.0)
- License: CC0-1.0
- Clips: 25,445 total (Korean: 6,998 | Hindi: 18,447)
- Anonymization standard:
orator_forge_k5_v1
Dataset Sources
- Source project: Mozilla Common Voice
- Source license: CC0-1.0
- Part of: Alexandria Voice Corpus (Orator Forge)
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.
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
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:
- MP3 source audio converted to 16kHz mono WAV (ffmpeg, -20 dBFS normalization)
- Quality grading via Brouhaha (SNR, C50, VAD) — only T1/T2 retained
- Acoustic feature extraction via Parselmouth/Praat at 16kHz
- 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
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_sourcefield 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.
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}
}
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 |