metadata

language:
  - en
license: apache-2.0
size_categories:
  - 1K<n<10K
task_categories:
  - automatic-speech-recognition
pretty_name: speechocean762
tags:
  - pronunciation-scoring
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: accuracy
      dtype: int64
    - name: completeness
      dtype: float64
    - name: fluency
      dtype: int64
    - name: prosodic
      dtype: int64
    - name: text
      dtype: string
    - name: total
      dtype: int64
    - name: words
      list:
        - name: accuracy
          dtype: int64
        - name: phones
          sequence: string
        - name: phones-accuracy
          sequence: float64
        - name: stress
          dtype: int64
        - name: text
          dtype: string
        - name: total
          dtype: int64
        - name: mispronunciations
          list:
            - name: canonical-phone
              dtype: string
            - name: index
              dtype: int64
            - name: pronounced-phone
              dtype: string
    - name: speaker
      dtype: string
    - name: gender
      dtype: string
    - name: age
      dtype: int64
    - name: audio
      dtype: audio
  splits:
    - name: train
      num_bytes: 291617098
      num_examples: 2500
    - name: test
      num_bytes: 289610485
      num_examples: 2500
  download_size: 611820406
  dataset_size: 581227583

speechocean762: A non-native English corpus for pronunciation scoring task

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.

Uses

pip install datasets
pip install librosa
pip install soundfile

>>> from datasets import load_dataset

>>> test_set = load_dataset("mispeech/speechocean762", split="test")

>>> len(test_set)
2500

>>> next(iter(test_set))
{'file': 'WAVE/SPEAKER0003/000030012.WAV',
 'audio': {
   'path': 'WAVE/SPEAKER0003/000030012.WAV',
   'array': array([-0.00119019, -0.00500488, -0.00283813, ...,  0.00274658, 0.        ,  0.00125122]),
   'sampling_rate': 16000},
 'text': 'MARK IS GOING TO SEE ELEPHANT',
 'speaker': '0003',
 'gender': 'm',
 'age': 6,
 'accuracy': 9,
 'fluency': 9,
 'prosodic': 9,
 'total': 9,
 'words': {'text': ['MARK', 'IS', 'GOING', 'TO', 'SEE', 'ELEPHANT'],
  'accuracy': [10, 10, 10, 10, 10, 10],
  'stress': [10, 10, 10, 10, 10, 10],
  'total': [10, 10, 10, 10, 10, 10],
  'phones': [['M', 'AA0', 'R', 'K'],
    ['IH0', 'Z'],
    ['G', 'OW0', 'IH0', 'NG'],
    ['T', 'UW0'],
    ['S', 'IY0'],
    ['EH1', 'L', 'IH0', 'F', 'AH0', 'N', 'T']],
  'phones-accuracy': [[2.0, 2.0, 1.8, 2.0],
    [2.0, 1.8],
    [2.0, 2.0, 2.0, 2.0],
    [2.0, 2.0],
    [2.0, 2.0],
    [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]],
  'mispronunciations': ['[]', '[]', '[]', '[]', '[]', '[]']}}

The scoring metric

The experts score at three levels: phoneme-level, word-level, and sentence-level.

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

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

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

For the phones with an accuracy score lower than 0.5, an extra "mispronunciations" indicates which is the most likely phoneme that 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
}

Citation

Please cite our paper if you find this work useful:

@inproceedings{speechocean762,
  title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment},
  booktitle={Proc. Interspeech 2021},
  year=2021,
  author={Junbo Zhang, Zhiwen Zhang, Yongqing Wang, Zhiyong Yan, Qiong Song, Yukai Huang, Ke Li, Daniel Povey, Yujun Wang}
}