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We discovered some fundamental bugs in the initial data. Please see the depreciation notice for details. Issues have been resolved.

5 new languages and a corrected copy of the Korean and Hindi datasets by way of apology.

DEPRECATION_NOTICE.md

@@ -0,0 +1,76 @@
+# Deprecation Notice — Korean & Hindi Two-Language Pack (v1.0)
+
+**Status:** Deprecated. Please use the v1.1 replacement files in this folder.
+
+---
+
+## What was deprecated and why
+
+The original two-language release (`korean_cv24.parquet` and `hindi_cv24.parquet`, published as
+*Alexandria Voice Corpus — Korean & Hindi Macro-Prosody Telemetry*) was built on a pipeline
+with three unresolved quality-gate bugs. Those files should no longer be used.
+
+### Bugs present in the v1.0 files
+
+**BUG-029 — Missing C50 floor on T2 gate**
+The STUDIO tier (T2) admitted clips regardless of C50 (room clarity) as long as SNR was
+sufficient. This allowed high-reverb recordings — rooms with significant echo or reflected
+sound — to be stamped STUDIO. Affected features include spectral tilt, HNR, CPPS, and
+F0 smoothness, all of which degrade in reverberant conditions. The fix requires
+C50 ≥ 20 dB in addition to SNR for any clip to reach T2.
+
+**BUG-030 — Missing speech-ratio floor on T2 gate**
+The T2 gate had no minimum speech density requirement. Clips where more than 70% of the
+audio was ambient room tone or silence were admitted to STUDIO tier. This inflated
+articulation_rate and distorted nPVI values for those clips. The fix requires
+speech_ratio ≥ 0.30 for any clip to reach T2.
+
+**High-SNR C50 bypass**
+A fast path for loud recordings (SNR ≥ 35 dB) bypassed the C50 check entirely. A recording
+made in a tiled bathroom at high volume could clear the SNR bar but still contain severe
+reverb. This path was patched to apply the same C50 ≥ 20 dB and speech_ratio ≥ 0.30
+requirements as the standard T2 path.
+
+The full T2 gate post-fix requires **all three simultaneously**:
+SNR ≥ 25 dB **and** C50 ≥ 20 dB **and** speech_ratio ≥ 0.30.
+
+---
+
+## What to use instead
+
+This folder contains corrected replacement files for both languages, plus five additional
+languages, as the **v1.1 multilingual pack**:
+
+| File | Language | Rows | Notes |
+|---|---|---|---|
+| `korean_cv24.parquet` | Korean | 6,805 | Replaces v1.0 (was 6,998 — difference is T2 gate correction + k5 suppression) |
+| `hindi_cv24.parquet` | Hindi | 18,435 | Replaces v1.0 (was 18,447) |
+| `hebrew_cv24.parquet` | Hebrew | 5,455 | New in v1.1 |
+| `manx_cv24.parquet` | Manx | 6,579 | New in v1.1 |
+| `tzeltal_cv24.parquet` | Tzeltal | 5,585 | New in v1.1 |
+| `mdn_cv24.parquet` | Maguindanao | 5,536 | New in v1.1 |
+| `lasi_cv24.parquet` | Lasi | 10,435 | New in v1.1 |
+
+See `README.md` in this folder for full dataset documentation.
+
+---
+
+## Row count differences between v1.0 and v1.1
+
+The v1.0 Korean file had 6,998 rows; v1.1 has 6,805 (-193 net).
+The v1.0 Hindi file had 18,447 rows; v1.1 has 18,435 (-12 net).
+
+The reduction reflects two changes applied together:
+1. Retroactive tier regrading — clips that passed the old T2 gate but fail the corrected
+   gate (missing C50 or speech_ratio) were downgraded to T3 and excluded from export.
+2. K-anonymity suppression — the k=5 quasi-identifier check is unchanged, but the
+   regraded tier distribution shifted some group sizes below threshold.
+
+Neither the source audio nor the Mozilla filenames have changed. The underlying clips are
+the same; what changed is which clips meet the corrected STUDIO quality standard.
+
+---
+
+## Contact
+
+c.kleingertner@gmail.com

README.md

@@ -5,6 +5,11 @@ task_categories:
 language:
 - ko
 - hi
+- he
+- gv
+- tzh
+- mdh
+- lss
 tags:
 - audio
 - speech
@@ -13,121 +18,161 @@ tags:
 - linguistics
 - phonetics
 - voice-analytics
-pretty_name: Alexandria Voice Corpus — Korean & Hindi Macro-Prosody Telemetry
+- multilingual
+pretty_name: Alexandria Voice Corpus — Multilingual Macro-Prosody Telemetry (v1.1 Replacement)
 size_categories:
 - 10K<n<100K
 ---
 
-# Alexandria Voice Corpus — Korean & Hindi Macro-Prosody Telemetry
+# Alexandria Voice Corpus — Multilingual 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).
+**Version 1.1 — Replacement release**
 
-No audio is included. This is a structured feature dataset for linguistic research, speech technology development, and cross-linguistic prosody analysis.
+This pack supersedes the earlier Korean & Hindi two-language release. That release was built on a pipeline with several unresolved quality-gate bugs (documented below). This version corrects all known issues and expands to seven typologically diverse languages.
+
+No audio is included. This is a structured acoustic feature dataset for linguistic research, speech technology, and cross-linguistic prosody analysis.
 
 ---
 
-## Dataset Details
+## What changed from the previous release
 
-### What is macro-prosody telemetry?
+The earlier `korean_cv24` and `hindi_cv24` files in the two-language pack were generated before three gate-level bugs were resolved:
+
+| Bug | Effect on data |
+|---|---|
+| **BUG-029** — Missing C50 floor on T2 gate | High-reverb recordings admitted to STUDIO tier. Clips recorded in tiled bathrooms or reverberant rooms were incorrectly marked T2, inflating that tier and contaminating features like F0 smoothness and spectral tilt. |
+| **BUG-030** — Missing speech-ratio floor on T2 gate | Sparse/silence-heavy clips admitted to STUDIO tier. Clips where >70% of the audio was ambient room tone were stamped T2. This inflated articulation_rate and skewed nPVI values. |
+| **High-SNR branch C50 fix** | The fast path for very loud recordings (SNR ≥ 35 dB) also lacked the C50 floor, so a loud reverberant recording could bypass the room quality check entirely. |
+
+All three are fixed in this release. The full T2 gate now requires simultaneously: SNR ≥ 25 dB **and** C50 ≥ 20 dB **and** speech_ratio ≥ 0.30. Tier labels have been retroactively corrected across all ledgers.
 
-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.
+If you downloaded the previous pack, please replace both files.
 
-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 Details
 
 ### Dataset Description
 
-- **Curated by:** Orator Forge
-- **Language(s):** Korean (`ko`), Hindi (`hi`)
-- **Source corpus:** Mozilla Common Voice CV24 (CC0-1.0)
+- **Curated by:** MoonScape Software
+- **Version:** 1.1
+- **Languages:** Korean, Hindi, Hebrew, Manx, Tzeltal, Maguindanao, Lasi
+- **Source corpora:** Mozilla Common Voice CV24 (CC0-1.0) and Spontaneous Speech SPS2 (CC0-1.0)
 - **License:** CC0-1.0
-- **Clips:** 25,445 total (Korean: 6,998 | Hindi: 18,447)
-- **Anonymization standard:** `orator_forge_k5_v1`
+- **Total clips:** 58,830
+- **Anonymization standard:** `moonscape_k5_v1`
 
-### Dataset Sources
+### What is macro-prosody telemetry?
+
+Macro-prosody refers to the suprasegmental properties of speech — pitch contour, rhythm, intensity, voice quality — measured at the utterance level. Each row in this dataset is one spoken clip with 30+ acoustic features extracted from it.
 
-- **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)
+This is distinct from transcription, phoneme alignment, or word-level data. It is designed for population-level acoustic analysis, cross-linguistic typology research, and training prosody-aware speech models.
 
 ---
 
-## Uses
+## Language Coverage
+
+| Language | Code | Family | Corpus | Speech type | Clips | T1% | MetaGender% | Notes |
+|---|---|---|---|---|---|---|---|---|
+| Korean | `ko` | Koreanic (isolate) | CV24 | Scripted | 6,805 | 68% | 61% | Replacement for v1.0 |
+| Hindi | `hi` | Indo-Aryan | CV24 | Scripted | 18,435 | 67% | 55% | Replacement for v1.0 |
+| Hebrew | `he` | Semitic | CV24 | Scripted | 5,455 | 73% | 93% | Strong demographic metadata; heavily male-skewed (see notes) |
+| Manx | `gv` | Celtic (Goidelic) | CV24 | Scripted | 6,579 | 98% | 0% | Near-extinct revival language; near-entirely PRISTINE quality |
+| Tzeltal | `tzh` | Mayan | CV24 | Scripted | 5,585 | 57% | 68% | All meta-gender records are female (see notes) |
+| Maguindanao | `mdh` | Austronesian | SPS2 | Spontaneous | 5,536 | 33% | 0% | No TSV demographics; gender is pitch-inferred only |
+| Lasi | `lss` | Indo-Iranian (Sindhi) | SPS2 | Spontaneous | 10,435 | 59% | 0% | Large spontaneous corpus; no TSV demographics |
 
-### Direct Use
+**Total: 58,830 clips** (395 suppressed by k-anonymity, 0.7% suppression rate)
 
-- 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
+### Typological notes
 
-### Out-of-Scope Use
+This pack was selected to span typologically distinct language families and speech types:
 
-- **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.
+- **Korean** is a language isolate with phrase-final focus marking and complex mora timing — a useful contrast to the stress-timed Indo-Aryan languages.
+- **Hindi** is the largest corpus here and provides strong statistical power for Indo-Aryan prosody baselines.
+- **Hebrew** is a VSO Semitic language with root-and-pattern morphology; the high metadata coverage makes it useful for demographic-stratified analyses.
+- **Manx** is a Celtic revival language with a tiny native speaker community. The 98% PRISTINE rate reflects the controlled recording conditions of motivated community contributors.
+- **Tzeltal** is a Mayan language with ergative-absolutive alignment and a distinctive tonal register system. It is rarely represented in acoustic datasets.
+- **Maguindanao** (SPS2) is spontaneous speech from a Philippine Austronesian language. The T2-heavy distribution reflects the naturalistic recording conditions of the SPS2 corpus.
+- **Lasi** (SPS2) is a Sindhi variety spoken in Balochistan. Shorter median clip duration (3.4s vs 5–6s for CV24 languages) reflects the spontaneous speech format.
 
 ---
 
 ## Dataset Structure
 
-Each parquet file contains one row per utterance. Files are Snappy-compressed.
+Each Parquet file contains one row per utterance. Files are Snappy-compressed.
+
+### Column reference
 
 | Column | Type | Description |
 |---|---|---|
 | `clip_id` | string | Anonymized sequential ID (e.g. `korean_cv24_004521`) |
-| `lang` | string | BCP-47 language code |
+| `lang_code` | 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` |
+| `corpus_id` | string | Source corpus (`cv24` or `sps2`) |
+| `speech_type` | string | `scripted` or `spontaneous` |
+| `tier` | int | Quality tier: 1 (PRISTINE) or 2 (STUDIO). T3/T4 suppressed at export. |
+| `tier_label` | string | `PRISTINE` or `STUDIO` |
+| `duration_ms` | int | Clip duration bucketed to nearest 100ms |
+| `gender` | string | `male` / `female` / `other` / `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) |
+| `age` | string | Age bracket from CV metadata (where available; blank for SPS2) |
+| `syllable_count_approx` | int | Approximate syllable count (vowel-count proxy; transcript removed) |
+| `pitch_mean` | float32 | Mean F0 (Hz) |
+| `pitch_std` | float32 | F0 standard deviation (Hz) |
+| `pitch_range` | float32 | F0 range max–min (Hz) |
+| `pitch_velocity_max` | float32 | Maximum 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) |
+| `intensity_range` | float32 | Dynamic range (dB) |
+| `intensity_velocity_max` | float32 | Maximum rate of intensity change |
 | `hnr_mean` | float32 | Harmonics-to-noise ratio (dB) |
-| `cpps` | float32 | Cepstral peak prominence smoothed — breathiness indicator |
+| `cpps` | float32 | Cepstral peak prominence smoothed (breathiness) |
 | `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) |
+| `spectral_tilt` | float32 | Spectral slope (voice effort indicator) |
 | `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 |
+| `teo_std` | float32 | Teager energy operator std dev |
+| `f1_mean` | float32 | First formant mean (Hz) |
+| `f2_mean` | float32 | Second formant mean (Hz) |
+| `f3_mean` | float32 | Third formant mean (Hz) |
+| `formant_dispersion` | float32 | F3–F1 dispersion (Hz) |
+| `npvi` | float32 | Normalized pairwise variability index (rhythm) |
+| `articulation_rate` | float32 | Syllables per second (speech intervals only) |
+| `snr_median` | float32 | Median SNR (Brouhaha) |
+| `c50_median` | float32 | Median C50 clarity (Brouhaha) |
 | `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 |
+| `emotion_score` | float32 | Brouhaha emotional arousal proxy |
+| `dialect_tag` | string | Accent/dialect slug from CV metadata (where available; blank for SPS2) |
+| `sample_type` | string | `core` (cream-selected) or null |
+
 
-### Quality Tiers
+### Quality tiers
 
-Clips were graded using [Brouhaha](https://github.com/marianne-m/brouhaha-vad) (SNR + C50 + VAD scoring):
+Clips were graded using [Brouhaha](https://github.com/marianne-m/brouhaha-vad) acoustic scoring. The T2 gate requires **all three** conditions simultaneously (corrected in v1.1):
 
-| 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 |
+| Tier | Label | SNR | C50 | Speech ratio | Description |
+|---|---|---|---|---|---|
+| T1 | PRISTINE | ≥ 35 dB | ≥ 35 dB | ≥ 0.30 | Studio/near-studio quality |
+| T2 | STUDIO | ≥ 25 dB | ≥ 20 dB | ≥ 0.30 | Clean field recording, low reverb |
+| T3 | WILD | ≥ 10 dB | any | ≥ 0.10 | Usable but noisy — **not exported** |
+| T4 | TRASH | < 10 dB | any | < 0.10 | Rejected — **not exported** |
 
-Only T1 and T2 clips are included in this release.
+Only T1 and T2 clips appear in this dataset. T3 and T4 are excluded at export.
 
 ### Files
 
 ```
-korean_cv24.parquet     — 6,998 rows
-hindi_cv24.parquet      — 18,447 rows
+korean_cv24.parquet         6,805 rows   ~600 KB
+hindi_cv24.parquet         18,435 rows   ~1.1 MB
+hebrew_cv24.parquet         5,455 rows   ~475 KB
+manx_cv24.parquet           6,579 rows   ~440 KB
+tzeltal_cv24.parquet        5,585 rows   ~500 KB
+mdn_cv24.parquet            5,536 rows   ~500 KB
+lasi_cv24.parquet          10,435 rows   ~860 KB
 ```
 
 ---
@@ -136,71 +181,65 @@ hindi_cv24.parquet      — 18,447 rows
 
 ### 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.
+This release addresses two gaps: the quality-gate bugs in the initial Korean/Hindi release, and the absence of any low-resource, non-Western language representation in the initial pack. Tzeltal, Manx, Lasi, and Maguindanao are rarely seen in structured acoustic datasets. The SPS2 spontaneous speech languages (Maguindanao, Lasi) provide a direct contrast to the CV24 scripted speech languages within this same release.
 
 ### 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
 
-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)
+1. MP3 source audio converted to 16 kHz mono WAV (ffmpeg, −20 dBFS normalization)
+2. Quality grading via Brouhaha (SNR, C50, VAD) — T1/T2 retained only
+3. Tier retroactively corrected post BUG-029/030 fix via `regrade_tiers.py`
+4. Acoustic feature extraction via Parselmouth/Praat at 16 kHz
+5. Cream selection (demographically balanced 25-minute representative subset per language) recorded in `sample_type` field
+6. Anonymization and precision degradation applied at export
 
 #### 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.
+CV24 recordings were made by volunteer contributors to the Mozilla Common Voice project under CC0. SPS2 recordings were collected by the Mozilla Spontaneous Speech project under CC0. Contributors self-reported demographic metadata where willing; many rows will have blank age/accent fields.
 
-### Anonymization
+### Anonymization — `moonscape_k5_v1`
 
-This dataset applies the `orator_forge_k5_v1` anonymization standard:
-
-- Original Mozilla filenames replaced with sequential anonymized clip IDs
+- Original Mozilla filenames replaced with sequential anonymized clip IDs (mapping kept internal, never distributed)
 - 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
+- All continuous acoustic variables truncated to 2 decimal places, stored as float32
+- Duration bucketed to nearest 100ms
+- K-anonymity at k=5 on `{gender, age_bucket, duration_bucket}` — rows in groups smaller than k=5 suppressed (395 rows suppressed across 7 languages, 0.7%)
 
 ---
 
 ## 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.
+**Gender coverage varies significantly by language.** Hebrew has 93% self-reported gender metadata but is heavily male-skewed (4,982 male vs 95 female meta-labeled records). Tzeltal has 68% metadata coverage but all meta-labeled records are female — this reflects the contributor demographics of the CV24 Tzeltal community at time of collection, not the language population. Manx, Maguindanao, and Lasi have 0% self-reported gender; all gender labels are pitch-inferred and should be treated accordingly. Always inspect `gender_source` before demographic analyses.
+
+**Scripted vs spontaneous speech are not directly comparable.** CV24 (Korean, Hindi, Hebrew, Manx, Tzeltal) is read speech from volunteer recordings of prompted sentences. SPS2 (Maguindanao, Lasi) is spontaneous conversational speech. Articulation rate, pause rate, nPVI, and pitch dynamics will differ systematically between the two corpus types — this is a real typological signal but also a corpus-type confound. The `speech_type` and `corpus_id` columns allow you to stratify analyses accordingly.
+
+**Recording conditions are uncontrolled for CV24.** Common Voice contributors record at home on personal devices. Acoustic conditions vary widely; the quality gate reduces but does not eliminate this variance. Manx is an exception — the 98% PRISTINE rate suggests a tightly controlled recording campaign by the community.
+
+**Lasi and Maguindanao lack TSV demographic metadata.** The SPS2 corpus was released without validated speaker demographic files. Age, accent, and dialect fields will be blank for these languages. Gender is pitch-inferred only.
+
+**Formant data (F1/F2/F3) is present but may be unreliable for tonal languages.** The Parselmouth formant tracker can produce artifacts in tonal contexts. Cross-validate against known formant benchmarks before using F1/F2/F3 for Korean, Tzeltal, or Maguindanao.
+
+**Prohibited use:** Do not attempt to use this dataset for speaker identification, speaker re-linking to source audio, or any form of individual re-identification. This is prohibited regardless of technical feasibility and violates the terms of use.
 
 ### 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.
+Use `gender_source == 'meta'` to filter to self-reported labels for any demographic analysis. Use `corpus_id` to separate scripted from spontaneous comparisons. For rhythm typology work, `npvi` and `articulation_rate` are the most reliable features in this release. Treat `intensity_mean` and `intensity_max` with caution — Mozilla applies normalization during encoding which compresses the true dynamic range.
 
 ---
 
 ## 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},
+@dataset{alexandria_multilingual_prosody_v1_1_2026,
+  title     = {Alexandria Voice Corpus --- Multilingual Macro-Prosody Telemetry v1.1},
+  author    = {MoonScape Software},
   year      = {2026},
   license   = {CC0-1.0},
-  note      = {Derived from Mozilla Common Voice CV24 (CC0). 
-               Acoustic features extracted via Parselmouth/Praat.}
+  note      = {Derived from Mozilla Common Voice CV24 and Spontaneous Speech SPS2 (CC0).
+               Acoustic features extracted via Parselmouth/Praat. v1.1 corrects
+               quality-gate bugs BUG-029, BUG-030, and high-SNR C50 bypass present
+               in the earlier Korean/Hindi release.}
 }
 
 @misc{mozilla_common_voice,
@@ -217,19 +256,21 @@ If you use this dataset, please cite the Mozilla Common Voice project as the sou
 
 | 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 |
+| **F0 / pitch_mean** | Fundamental frequency — perceived pitch, Hz |
+| **HNR** | Harmonics-to-noise ratio — higher = cleaner, more periodic voice |
+| **CPPS** | Cepstral peak prominence smoothed — lower = breathier voice |
+| **nPVI** | Normalized pairwise variability index — durational variability between adjacent syllables; higher in stress-timed languages |
+| **C50** | Clarity metric — higher = less reverb/echo |
 | **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 |
+| **Brouhaha** | Quality scoring model: [github.com/marianne-m/brouhaha-vad](https://github.com/marianne-m/brouhaha-vad) |
+| **T1 / PRISTINE** | SNR ≥ 35, C50 ≥ 35, speech_ratio ≥ 0.30 |
+| **T2 / STUDIO** | SNR ≥ 25, C50 ≥ 20, speech_ratio ≥ 0.30 (all three required simultaneously) |
+| **moonscape_k5_v1** | Anonymization: k=5 suppression + sequential IDs + 2dp truncation + 100ms duration bucketing |
+| **CV24** | Mozilla Common Voice 24.0 — scripted read speech |
+| **SPS2** | Mozilla Spontaneous Speech 2.0 — unscripted conversational speech |
 
 ---
 
-## Dataset Card Contact
+## Contact
 
-c.kleingertner@gmail.com
-``
+moonscapesoftware@gmail.com

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