| --- |
| language: |
| - en |
| license: apache-2.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - automatic-speech-recognition |
| pretty_name: speechocean762 |
| tags: |
| - pronunciation-scoring |
| - arxiv:2104.01378 |
| 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: 333140374 |
| num_examples: 2500 |
| - name: test |
| num_bytes: 311843439 |
| num_examples: 2500 |
| download_size: 644387157 |
| dataset_size: 644983813 |
| --- |
| # 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 |
| ```python |
| >>> from datasets import load_dataset |
| |
| >>> test_set = load_dataset("mispeech/speechocean762", split="test") |
| |
| >>> len(test_set) |
| 2500 |
| |
| >>> next(iter(test_set)) |
| {'accuracy': 9, |
| 'completeness': 10.0, |
| 'fluency': 9, |
| 'prosodic': 9, |
| 'text': 'MARK IS GOING TO SEE ELEPHANT', |
| 'total': 9, |
| 'words': [{'accuracy': 10, |
| 'phones': ['M', 'AA0', 'R', 'K'], |
| 'phones-accuracy': [2.0, 2.0, 1.8, 2.0], |
| 'stress': 10, |
| 'text': 'MARK', |
| 'total': 10, |
| 'mispronunciations': []}, |
| {'accuracy': 10, |
| 'phones': ['IH0', 'Z'], |
| 'phones-accuracy': [2.0, 1.8], |
| 'stress': 10, |
| 'text': 'IS', |
| 'total': 10, |
| 'mispronunciations': []}, |
| {'accuracy': 10, |
| 'phones': ['G', 'OW0', 'IH0', 'NG'], |
| 'phones-accuracy': [2.0, 2.0, 2.0, 2.0], |
| 'stress': 10, |
| 'text': 'GOING', |
| 'total': 10, |
| 'mispronunciations': []}, |
| {'accuracy': 10, |
| 'phones': ['T', 'UW0'], |
| 'phones-accuracy': [2.0, 2.0], |
| 'stress': 10, |
| 'text': 'TO', |
| 'total': 10, |
| 'mispronunciations': []}, |
| {'accuracy': 10, |
| 'phones': ['S', 'IY0'], |
| 'phones-accuracy': [2.0, 2.0], |
| 'stress': 10, |
| 'text': 'SEE', |
| 'total': 10, |
| 'mispronunciations': []}, |
| {'accuracy': 10, |
| 'phones': ['EH1', 'L', 'IH0', 'F', 'AH0', 'N', 'T'], |
| 'phones-accuracy': [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0], |
| 'stress': 10, |
| 'text': 'ELEPHANT', |
| 'total': 10, |
| 'mispronunciations': []}], |
| 'speaker': '0003', |
| 'gender': 'm', |
| 'age': 6, |
| 'audio': {'path': '000030012.wav', |
| 'array': array([-0.00119019, -0.00500488, -0.00283813, ..., 0.00274658, |
| 0. , 0.00125122]), |
| 'sampling_rate': 16000}} |
| ``` |
|
|
| ## 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: |
|
|
| ```json |
| { |
| "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: |
|
|
| ```bibtext |
| @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} |
| } |
| ``` |
