README.md

---
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](https://commonvoice.mozilla.org)
- **Source license:** [CC0-1.0](https://creativecommons.org/publicdomain/zero/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](https://github.com/marianne-m/brouhaha-vad) (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](https://commonvoice.mozilla.org), 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

---

## 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.

---

## Citation

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

**BibTeX:**
```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](https://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 |

---

## Dataset Card Contact

c.kleingertner@gmail.com
``