---
license: cc-by-nc-4.0
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- non-native
- pronunciation
- speech
- pronunciation assessment
- phoneme
pretty_name: EpaDB
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: train.json
  - split: test
    path: test.json
---

# EpaDB: English Pronunciation by Argentinians

## Dataset Summary

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 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.

## Languages

- L2 utterances: English
- Speaker L1: Spanish

## Dataset Structure

### Data Instances

Each JSON entry describes one utterance:

- 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 orthographic transcription (`transcription`).

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `utt_id` | string | Unique utterance identifier (e.g., `spkr28_1`). |
| `speaker_id` | string | Speaker identifier. |
| `sentence_id` | string | Reference sentence ID (matches `reference_transcriptions.txt`). |
| `phone_ids` | sequence[string] | Unique phone identifiers per utterance. |
| `reference` | sequence[string] | reference phones assigned to match the closer aimed pronunciation by the speaker. |
| `annot_1` | sequence[string] | Annotator 1 phones (`-` marks deletions). |
| `annot_2` | sequence[string] | Annotator 3 phones when available, empty otherwise. |
| `label_1` | sequence[string] | Annotator 1 phone labels (`"1"` correct, `"0"` incorrect). |
| `label_2` | sequence[string] | Annotator 3 phone labels when present. |
| `error_type` | sequence[string] | Derived categories: `correct`, `insertion`, `deletion`, `distortion`, `substitution`. |
| `start_mfa` | sequence[float] | Phone start times (seconds). |
| `end_mfa` | sequence[float] | Phone end times (seconds). |
| `global_1` | float or null | Annotator 1 utterance-level score (1–4). |
| `global_2` | float or null | Annotator 3 score when available. |
| `level_1` | string or null | Speaker-level proficiency tier from annotator 1 ("A"/"B"). |
| `level_2` | string or null | Speaker tier from annotator 3. |
| `gender` | string or null | Speaker gender (`"M"`/`"F"`). |
| `duration` | float | Utterance duration in seconds (after resampling to 16 kHz). |
| `sample_rate` | int | Sample rate in Hz (16,000). |
| `audio` | string | Waveform filename (`<utt_id>.wav`). |
| `transcription` | string or null | Reference sentence text. |

### Data Splits

| Split | # Examples |
|-------|------------|
| train | 1,903 |
| test  | 1,263 |

### Notes

- 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.
- 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).

## Citation

```
@article{vidal2019epadb,
  title   = {EpaDB: a database for development of pronunciation assessment systems},
  author  = {Vidal, Jazmin and Ferrer, Luciana and Brambilla, Leonardo},
  journal = {Proc. Interspeech},
  pages   = {589--593},
  year    = {2019}
}
```

## Usage

Install dependencies and load the dataset:

```python
from datasets import load_dataset
ds = load_dataset("hashmin/epadb", split="train")
```

## Acknowledgements

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