| --- |
| 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 |
