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Datasets:
hashmin
/
epadb

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Tabular
Text
Formats:
json
Languages:
English
Size:
1K - 10K
Tags:
non-native
pronunciation
speech
pronunciation assessment
phoneme
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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  1. README.md +35 -19
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  50. train.json +0 -0
README.md CHANGED
@@ -1,31 +1,40 @@
1
  ---
 
 
 
 
 
 
 
 
2
  license: cc-by-nc-4.0
 
 
 
3
  task_categories:
4
  - automatic-speech-recognition
5
  - audio-classification
6
- tags:
7
- - non-native-speech
8
- - pronunciation-assessment
9
- - capt
10
- - phoneme
11
  ---
12
 
13
- # EPADB: English Pronunciation by Argentinians
14
 
15
  ## Dataset Summary
16
 
17
- EpaDB is a speech database intended for research in pronunciation assessment at the phoneme level. The database 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. Each utterance has phone-level annotations from up to two expert annotators along with per-utterance global proficiency scores. Additionally to MFA (Montreal Forced Aligner) timestamps, metadata includes derived error classifications, gender, and reference orthographic transcriptions. The database is organized in a `train` and a `test` partition and includes speaker-wise waveform recordings resampled to 16 kHz.
18
 
19
  ## Supported Tasks
20
 
21
- - **Pronunciation Assessment** – predict utterance-level global scores or phone level accuracy scores
22
  - **Phone-level Error Detection** – classify each phone as insertion, deletion, distortion, substitution, or correct.
23
  - **Alignment Analysis** – leverage MFA timings to study forced alignment quality or to refine pronunciation models.
24
 
25
  ## Languages
26
 
27
  - L2 utterances: English
28
- - Speaker L1: Spanish from Río de la Plata region Argentina
29
 
30
  ## Dataset Structure
31
 
@@ -33,13 +42,13 @@ EpaDB is a speech database intended for research in pronunciation assessment at
33
 
34
  Each JSON entry describes one utterance:
35
 
36
- - Manually chose phoneme reference sequences (`reference`) and manual phonetic transcriptions (`annot_1`, optional `annot_2`).
37
  - Phone-level labels (`label_1`, `label_2`) and derived `error_type` categories.
38
  - MFA start/end timestamps per phone (`start_mfa`, `end_mfa`).
39
  - Per-utterance global scores (`global_1`, `global_2`) and propagated speaker levels (`level_1`, `level_2`).
40
  - Speaker metadata (`speaker_id`, `gender`).
41
  - Audio metadata (`duration`, `sample_rate`, `wav_path`) plus the waveform itself.
42
- - Reference orthographic transcription (`transcription`).
43
 
44
  ### Data Fields
45
 
@@ -47,9 +56,9 @@ Each JSON entry describes one utterance:
47
  |-------|------|-------------|
48
  | `utt_id` | string | Unique utterance identifier (e.g., `spkr28_1`). |
49
  | `speaker_id` | string | Speaker identifier. |
50
- | `sentence_id` | string | Reference sentence ID. |
51
  | `phone_ids` | sequence[string] | Unique phone identifiers per utterance. |
52
- | `reference` | sequence[string] | reference phones. |
53
  | `annot_1` | sequence[string] | Annotator 1 phones (`-` marks deletions). |
54
  | `annot_2` | sequence[string] | Annotator 3 phones when available, empty otherwise. |
55
  | `label_1` | sequence[string] | Annotator 1 phone labels (`"1"` correct, `"0"` incorrect). |
@@ -57,7 +66,7 @@ Each JSON entry describes one utterance:
57
  | `error_type` | sequence[string] | Derived categories: `correct`, `insertion`, `deletion`, `distortion`, `substitution`. |
58
  | `start_mfa` | sequence[float] | Phone start times (seconds). |
59
  | `end_mfa` | sequence[float] | Phone end times (seconds). |
60
- | `global_1` | float or null | Annotator 1 utterance-level score (1–5). |
61
  | `global_2` | float or null | Annotator 3 score when available. |
62
  | `level_1` | string or null | Speaker-level proficiency tier from annotator 1 ("A"/"B"). |
63
  | `level_2` | string or null | Speaker tier from annotator 3. |
@@ -78,14 +87,19 @@ Each JSON entry describes one utterance:
78
  ### Notes
79
 
80
  - When annotator 3 did not label an utterance, related fields (`annot_2`, `label_2`, `global_2`, `level_2`) are absent or set to null.
81
- - Error types come from simple heuristics contrasting the reference phones with annotator 1 labels.
82
- - Waveforms were resampled to 16 kHz using `ffmpeg` during data preparation.
83
- - Global scores are averaged per speaker to derive `level_*` tiers (`A` if mean ≥ 3, `B` otherwise).
 
 
 
 
 
84
 
85
  ## Licensing
86
 
87
  - Audio and annotations: CC BY-NC 4.0 (non-commercial use allowed with attribution).
88
-
89
 
90
  ## Citation
91
 
@@ -115,8 +129,10 @@ ds = load_dataset(
115
  print(ds)
116
  print(ds[0]["utt_id"], ds[0]["audio"]["sampling_rate"]) # 16000
117
 
 
 
118
  ```
119
 
120
  ## Acknowledgements
121
 
122
- 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.
 
1
  ---
2
+ ymi_version: 1.0
3
+ language:
4
+ - en
5
+ tags:
6
+ - speech
7
+ - pronunciation
8
+ - error-detection
9
+ - forced-alignment
10
  license: cc-by-nc-4.0
11
+ pretty_name: "EPADB: English Pronunciation Assessment Dataset"
12
+ size_categories:
13
+ - 1K<n<10K
14
  task_categories:
15
  - automatic-speech-recognition
16
  - audio-classification
17
+ task_ids:
18
+ - speech-recognition-other
19
+ - audio-classification-other
 
 
20
  ---
21
 
22
+ # EPADB: English Pronunciation Assessment Dataset for Batched Diagnosis
23
 
24
  ## Dataset Summary
25
 
26
+ 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.
27
 
28
  ## Supported Tasks
29
 
30
+ - **Pronunciation Assessment** – predict utterance-level global scores or speaker-level proficiency tiers.
31
  - **Phone-level Error Detection** – classify each phone as insertion, deletion, distortion, substitution, or correct.
32
  - **Alignment Analysis** – leverage MFA timings to study forced alignment quality or to refine pronunciation models.
33
 
34
  ## Languages
35
 
36
  - L2 utterances: English
37
+ - Speaker L1: Spanish
38
 
39
  ## Dataset Structure
40
 
 
42
 
43
  Each JSON entry describes one utterance:
44
 
45
+ - Phone sequences for MFA reference (`reference`) and annotators (`annot_1`, optional `annot_2`).
46
  - Phone-level labels (`label_1`, `label_2`) and derived `error_type` categories.
47
  - MFA start/end timestamps per phone (`start_mfa`, `end_mfa`).
48
  - Per-utterance global scores (`global_1`, `global_2`) and propagated speaker levels (`level_1`, `level_2`).
49
  - Speaker metadata (`speaker_id`, `gender`).
50
  - Audio metadata (`duration`, `sample_rate`, `wav_path`) plus the waveform itself.
51
+ - Reference sentence transcription (`transcription`).
52
 
53
  ### Data Fields
54
 
 
56
  |-------|------|-------------|
57
  | `utt_id` | string | Unique utterance identifier (e.g., `spkr28_1`). |
58
  | `speaker_id` | string | Speaker identifier. |
59
+ | `sentence_id` | string | Reference sentence ID (matches `reference_transcriptions.txt`). |
60
  | `phone_ids` | sequence[string] | Unique phone identifiers per utterance. |
61
+ | `reference` | sequence[string] | MFA reference phones. |
62
  | `annot_1` | sequence[string] | Annotator 1 phones (`-` marks deletions). |
63
  | `annot_2` | sequence[string] | Annotator 3 phones when available, empty otherwise. |
64
  | `label_1` | sequence[string] | Annotator 1 phone labels (`"1"` correct, `"0"` incorrect). |
 
66
  | `error_type` | sequence[string] | Derived categories: `correct`, `insertion`, `deletion`, `distortion`, `substitution`. |
67
  | `start_mfa` | sequence[float] | Phone start times (seconds). |
68
  | `end_mfa` | sequence[float] | Phone end times (seconds). |
69
+ | `global_1` | float or null | Annotator 1 utterance-level score (1–4). |
70
  | `global_2` | float or null | Annotator 3 score when available. |
71
  | `level_1` | string or null | Speaker-level proficiency tier from annotator 1 ("A"/"B"). |
72
  | `level_2` | string or null | Speaker tier from annotator 3. |
 
87
  ### Notes
88
 
89
  - When annotator 3 did not label an utterance, related fields (`annot_2`, `label_2`, `global_2`, `level_2`) are absent or set to null.
90
+ - Error types come from simple heuristics contrasting MFA reference phones with annotator 1 labels.
91
+ - Waveforms were resampled to 16 kHz using `ffmpeg` during manifest generation.
92
+
93
+ ## Data Processing
94
+
95
+ 1. Forced alignments and annotations were merged to produce enriched CSV files per speaker/partition.
96
+ 2. `create_db.py` aggregates these into JSON manifests, adds error types, and resamples audio.
97
+ 3. Global scores are averaged per speaker to derive `level_*` tiers (`A` if mean ≥ 3, `B` otherwise).
98
 
99
  ## Licensing
100
 
101
  - Audio and annotations: CC BY-NC 4.0 (non-commercial use allowed with attribution).
102
+ - Please ensure any downstream usage complies with participant consent and institutional policies.
103
 
104
  ## Citation
105
 
 
129
  print(ds)
130
  print(ds[0]["utt_id"], ds[0]["audio"]["sampling_rate"]) # 16000
131
 
132
+ # After pushing to the Hugging Face Hub:
133
+ # ds = load_dataset("JazminVidal/epadb", split="train")
134
  ```
135
 
136
  ## Acknowledgements
137
 
138
+ 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.
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test.json ADDED
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train.json ADDED
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