Update README.md

README.md

@@ -1,5 +1,4 @@
 ---
-ymi_version: 1.0
 language:
   - en
 tags:
@@ -8,9 +7,6 @@ tags:
   - error-detection
   - forced-alignment
 license: cc-by-nc-4.0
-pretty_name: "EPADB: English Pronunciation Assessment Dataset"
-size_categories:
-  - 1K<n<10K
 task_categories:
   - automatic-speech-recognition
   - audio-classification
@@ -19,15 +15,18 @@ task_ids:
   - audio-classification-other
 ---
 
-# EPADB: English Pronunciation Assessment Dataset for Batched Diagnosis
+# EpaDB: English Pronunciation by Argentinians
 
 ## Dataset Summary
 
-EPADB contains curated pronunciation assessment data collected from Spanish-speaking learners of English. Each utterance has manually aligned phone-level annotations from up to two expert annotators along with per-utterance global proficiency scores. Metadata links the aligned phones with MFA (Montreal Forced Aligner) timestamps, derived error classifications, and reference transcriptions. The corpus ships with a `train` and a `test` partition and includes speaker-wise waveform recordings resampled to 16 kHz.
+EpaDB is a speech database intended for research in pronunciation scoring. The corpus includes audios from 50 Spanish speakers (25 males and 25 females) from 
+Argentina reading phrases in English. Each speaker recorded 64 short phrases containing sounds hard to pronounce for this population adding up to ~3.5 hours of speech.
+
 
 ## Supported Tasks
 
-- **Pronunciation Assessment** – predict utterance-level global scores or speaker-level proficiency tiers.
+- **Pronunciation Assessment** – predict utterance-level global scores or phoneme-level correct/incorrect
+- **Phone Recognition** - predict phoneme sequences
 - **Phone-level Error Detection** – classify each phone as insertion, deletion, distortion, substitution, or correct.
 - **Alignment Analysis** – leverage MFA timings to study forced alignment quality or to refine pronunciation models.
 
@@ -42,13 +41,13 @@ EPADB contains curated pronunciation assessment data collected from Spanish-spea
 
 Each JSON entry describes one utterance:
 
-- Phone sequences for MFA reference (`reference`) and annotators (`annot_1`, optional `annot_2`).
+- Phone sequences for reference transcription (`reference`) and annotators (`annot_1`, optional `annot_2`).
 - Phone-level labels (`label_1`, `label_2`) and derived `error_type` categories.
 - MFA start/end timestamps per phone (`start_mfa`, `end_mfa`).
 - Per-utterance global scores (`global_1`, `global_2`) and propagated speaker levels (`level_1`, `level_2`).
 - Speaker metadata (`speaker_id`, `gender`).
 - Audio metadata (`duration`, `sample_rate`, `wav_path`) plus the waveform itself.
-- Reference sentence transcription (`transcription`).
+- Reference sentence orthographic transcription (`transcription`).
 
 ### Data Fields
 
@@ -89,17 +88,12 @@ Each JSON entry describes one utterance:
 - When annotator 3 did not label an utterance, related fields (`annot_2`, `label_2`, `global_2`, `level_2`) are absent or set to null.
 - Error types come from simple heuristics contrasting MFA reference phones with annotator 1 labels.
 - Waveforms were resampled to 16 kHz using `ffmpeg` during manifest generation.
-
-## Data Processing
-
-1. Forced alignments and annotations were merged to produce enriched CSV files per speaker/partition.
-2. `create_db.py` aggregates these into JSON manifests, adds error types, and resamples audio.
-3. Global scores are averaged per speaker to derive `level_*` tiers (`A` if mean ≥ 3, `B` otherwise).
+- Forced alignments and annotations were merged to produce enriched CSV files per speaker/partition.
+- Global scores are averaged per speaker to derive `level_*` tiers (`A` if mean ≥ 3, `B` otherwise).
 
 ## Licensing
 
 - Audio and annotations: CC BY-NC 4.0 (non-commercial use allowed with attribution).
-- Please ensure any downstream usage complies with participant consent and institutional policies.
 
 ## Citation
 
@@ -135,4 +129,5 @@ print(ds[0]["utt_id"], ds[0]["audio"]["sampling_rate"])  # 16000
 
 ## Acknowledgements
 
-We thank the learners and expert annotators who contributed to EPADB, as well as the speech processing community for tools such as MFA and ffmpeg used in the data preparation pipeline.
+The database is an effort of the Speech Lab at the Laboratorio de Inteligencia Artificial Aplicada from 
+the Universidad de Buenos Aires and was partially funded by Google by a Google Latin America Reseach Award in 2018