language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- audio-classification
- automatic-speech-recognition
pretty_name: ' '
tags:
- pronunciation-scoring
- L1-Mandarin
- L2-English
dataset_info:
features:
- name: spk
dtype: string
- name: age
dtype: string
- name: gender
dtype: string
- name: utt_name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: utt_text
dtype: string
- name: utt_accuracy
dtype: int64
- name: utt_completeness
dtype: float64
- name: utt_fluency
dtype: int64
- name: utt_prosodic
dtype: int64
- name: utt_total
dtype: int64
- name: words
sequence: string
- name: words_accuracy
sequence: int64
- name: words_stress
sequence: int64
- name: words_total
sequence: int64
- name: phones
sequence:
sequence: string
- name: phones_godness
sequence:
sequence: float64
- name: duration
dtype: float64
splits:
- name: train
num_bytes: 333075617.5
num_examples: 2500
- name: test
num_bytes: 311790040.5
num_examples: 2500
download_size: 611757634
dataset_size: 644865658
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
speechocean762: A non-native English corpus for pronunciation scoring task
How to use?
you can load data using
speechocean762_dataset = load_dataset('seba3y/speechocean762')
>> speechocean762_dataset
DatasetDict({
train: Dataset({
features: ['spk', 'age', 'gender', 'utt_name', 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total', 'words', 'words_accuracy', 'words_stress', 'words_total', 'phones', 'phones_godness'],
num_rows: 2500
})
test: Dataset({
features: ['spk', 'age', 'gender', 'utt_name', 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total', 'words', 'words_accuracy', 'words_stress', 'words_total', 'phones', 'phones_godness'],
num_rows: 2500
})
})
Features are ordered as following:
1- Demographic featurs: 'spk', 'age', 'gender', 'utt_name'
2- Sentence-level featurs: 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total'
3- Word-level featurs: 'words', 'words_accuracy', 'words_stress', 'words_total'
4- Phoneme-level featurs: 'phones', 'phones_godness'
>> speechocean762_dataset['train'][0]
{'spk': '0001',
'age': 6,
'gender': 'm',
'utt_name': '000010011',
'audio': {'path': '/content/speechocean762/WAVE/SPEAKER0001/000010011.WAV',
'array': array([-9.46044922e-04, -2.38037109e-03, -1.31225586e-03, ...,
-9.15527344e-05, 3.05175781e-04, -2.44140625e-04]),
'sampling_rate': 16000},
'utt_text': 'WE CALL IT BEAR',
'utt_accuracy': 8,
'utt_completeness': 10.0,
'utt_fluency': 9,
'utt_prosodic': 9,
'utt_total': 8,
'words': "['WE', 'CALL', 'IT', 'BEAR']",
'words_accuracy': '[10, 10, 10, 6]',
'words_stress': '[10, 10, 10, 10]',
'words_total': '[10, 10, 10, 6]',
'phones': "[['W', 'IY0'], ['K', 'AO0', 'L'], ['IH0', 'T'], ['B', 'EH0', 'R']]",
'phones_godness': '[[2.0, 2.0], [2.0, 1.8, 1.8], [2.0, 2.0], [2.0, 1.0, 1.0]]'}
For word-level features, the 'words' in each sample is a list containing words, while 'words_accuracy', 'words_stress', and 'words_total' are lists of the same length as the words. The mapping is such that the first word corresponds to the first value in 'words_accuracy', and so on. On the other hand, for phoneme-level features, the 'phones' in each sample is a 2D list, with each sublist corresponding to a single word
Introduction
Pronunciation scoring is a crucial technology in computer-assisted language learning (CALL) systems. The pronunciation quality scores might be given at phoneme-level, word-level, and sentence-level for a typical pronunciation scoring task.
This corpus aims to provide a free public dataset for the pronunciation scoring task. Key features:
- It is available for free download for both commercial and non-commercial purposes.
- The speaker variety encompasses young children and adults.
- The manual annotations are in multiple aspects at sentence-level, word-level and phoneme-level.
This corpus consists of 5000 English sentences. All the speakers are non-native, and their mother tongue is Mandarin. Half of the speakers are Children, and the others are adults. The information of age and gender are provided.
Five experts made the scores. To avoid subjective bias, each expert scores independently under the same metric.
The scoring metric
The experts score at three levels: phoneme-level, word-level, and sentence-level.
Phoneme level
Score the pronunciation goodness of each phoneme within the words.
Score range: 0-2
- 2: pronunciation is correct
- 1: pronunciation is right but has a heavy accent
- 0: pronunciation is incorrect or missed
Word level
Score the accuracy and stress of each word's pronunciation.
Accuracy
Score range: 0 - 10
- 10: The pronunciation of the word is perfect
- 7-9: Most phones in this word are pronounced correctly but have accents
- 4-6: Less than 30% of phones in this word are wrongly pronounced
- 2-3: More than 30% of phones in this word are wrongly pronounced. In another case, the word is mispronounced as some other word. For example, the student mispronounced the word "bag" as "bike"
- 1: The pronunciation is hard to distinguish
- 0: no voice
Stress
Score range: {5, 10}
- 10: The stress is correct, or this is a mono-syllable word
- 5: The stress is wrong
Sentence level
Score the accuracy, fluency, completeness and prosodic at the sentence level.
Accuracy
Score range: 0 - 10
- 9-10: The overall pronunciation of the sentence is excellent, with accurate phonology and no obvious pronunciation mistakes
- 7-8: The overall pronunciation of the sentence is good, with a few pronunciation mistakes
- 5-6: The overall pronunciation of the sentence is understandable, with many pronunciation mistakes and accent, but it does not affect the understanding of basic meanings
- 3-4: Poor, clumsy and rigid pronunciation of the sentence as a whole, with serious pronunciation mistakes
- 0-2: Extremely poor pronunciation and only one or two words are recognizable
Completeness
Score range: 0.0 - 1.0 The percentage of the words with good pronunciation.
Fluency
Score range: 0 - 10
- 8-10: Fluent without noticeable pauses or stammering
- 6-7: Fluent in general, with a few pauses, repetition, and stammering
- 4-5: the speech is a little influent, with many pauses, repetition, and stammering
- 0-3: intermittent, very influent speech, with lots of pauses, repetition, and stammering
Prosodic
Score range: 0 - 10
- 9-10: Correct intonation at a stable speaking speed, speak with cadence, and can speak like a native
- 7-8: Nearly correct intonation at a stable speaking speed, nearly smooth and coherent, but with little stammering and few pauses
- 5-6: Unstable speech speed, many stammering and pauses with a poor sense of rhythm
- 3-4: Unstable speech speed, speak too fast or too slow, without the sense of rhythm
- 0-2: Poor intonation and lots of stammering and pauses, unable to read a complete sentence
Data structure
The following tree shows the file structure of this corpus on github:
├── scores.json
├── scores-detail.json
├── train
│ ├── spk2age
│ ├── spk2gender
│ ├── spk2utt
│ ├── text
│ ├── utt2spk
│ └── wav.scp
├── test
│ ├── spk2age
│ ├── spk2gender
│ ├── spk2utt
│ ├── text
│ ├── utt2spk
│ └── wav.scp
└── WAVE
├── SPEAKER0001
│ ├── 000010011.WAV
│ ├── 000010035.WAV
│ ├── ...
│ └── 000010173.WAV
├── SPEAKER0003
│ ├── 000030012.WAV
│ ├── 000030024.WAV
│ ├── ...
│ └── 000030175.WAV
└── SPEAKER0005
├── 000050003.WAV
├── 000050010.WAV
├── ...
└── 000050175.WAV
There are two datasets: train and test, and both are in Kaldi's data directory style.
The scores are stored in scores.json. Here is an example:
{
"000010011": { # utt-id
"text": "WE CALL IT BEAR", # transcript text
"accuracy": 8, # sentence-level accuracy score
"completeness": 10.0, # sentence-level completeness score
"fluency": 9, # sentence-level fluency score
"prosodic": 9, # sentence-level prosodic score
"total": 8, # sentence-level total score
"words": [
{
"accuracy": 10, # word-level accuracy score
"stress": 10, # word-level stress score
"total": 10, # word-level total score
"text": "WE", # the word text
"phones": "W IY0", # phones of the word
"phones-accuracy": [2.0, 2.0] # phoneme-level accuracy score
},
{
"accuracy": 10,
"stress": 10,
"total": 10,
"text": "CALL",
"phones": "K AO0 L",
"phones-accuracy": [2.0, 1.8, 1.8]
},
{
"accuracy": 10,
"stress": 10,
"total": 10,
"text": "IT",
"phones": "IH0 T",
"phones-accuracy": [2.0, 2.0]
},
{
"accuracy": 6,
"stress": 10,
"total": 6,
"text": "BEAR",
"phones": "B EH0 R",
"phones-accuracy": [2.0, 1.0, 1.0]
}
]
},
...
}
For the phones with an accuracy score lower than 0.5, an extra "mispronunciations" block indicates which phoneme the current phone was actually pronounced. An example:
{
"text": "LISA",
"accuracy": 5,
"phones": ["L", "IY1", "S", "AH0"],
"phones-accuracy": [0.4, 2, 2, 1.2],
"mispronunciations": [
{
"canonical-phone": "L",
"index": 0,
"pronounced-phone": "D"
}
],
"stress": 10,
"total": 6
}
The file scores.json is processed from scores-detail.json.
The two JSON files are almost the same, but scores-detail.json has the five experts' original scores, while the scores of scores.json were the average or median scores.
An example item in scores-detail.json:
{
"000010011": {
"text": "WE CALL IT BEAR",
"accuracy": [7.0, 9.0, 8.0, 8.0, 9.0],
"completeness": [1.0, 1.0, 1.0, 1.0, 1.0],
"fluency": [10.0, 9.0, 8.0, 8.0, 10.0],
"prosodic": [10.0, 9.0, 7.0, 8.0, 9.0],
"total": [7.6, 9.0, 7.9, 8.0, 9.1],
"words": [
{
"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 10.0, 10.0, 10.0, 10.0],
"text": "WE",
"ref-phones": "W IY0",
"phones": ["W IY0", "W IY0", "W IY0", "W IY0", "W IY0"]
},
{
"accuracy": [10.0, 8.0, 10.0, 10.0, 8.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 8.4, 10.0, 10.0, 8.4],
"text": "CALL",
"ref-phones": "K AO0 L",
"phones": ["K AO0 L", "K {AO0} L", "K AO0 L", "K AO0 L", "K AO0 {L}"],
},
{
"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 10.0, 10.0, 10.0, 10.0],
"text": "IT",
"ref-phones": "IH0 T",
"phones": ["IH0 T", "IH0 T", "IH0 T", "IH0 T", "IH0 T"]
},
{
"accuracy": [3.0, 7.0, 10.0, 2.0, 6.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"phones": ["B (EH0) (R)", "B {EH0} {R}", "B EH0 R", "B (EH0) (R)", "B EH0 [L] R"],
"total": [4.4, 7.6, 10.0, 3.6, 6.8],
"text": "BEAR",
"ref-phones": "B EH0 R"
}
],
},
...
}
In scores-detail.json, the phoneme-level scores are notated in the following convenient notation:
- for score 2, do not use any symbol
- for score 1, use "{}" symbol
- for score 0, use "()" symbol
- for the inserted phone, use the "[]" symbol
For example, "B (EH) R" means the score of EH is 0 while the scores of B and R are both 2, "B EH [L] R" mean there is an unexpected phone "L" and the other phones are scored 2.
Citation
Please cite our paper if you find this work useful:
@inproceedings{zhang2021speechocean762,
title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment},
author={Zhang, Junbo and Zhang, Zhiwen and Wang, Yongqing and Yan, Zhiyong and Song, Qiong and Huang, Yukai and Li, Ke and Povey, Daniel and Wang, Yujun},
booktitle={Proc. Interspeech 2021},
year={2021}
}