| # speechocean762: A non-native English corpus for pronunciation scoring task |
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| ## 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. |
| However, there is no public dataset for this task. Researchers use their private datasets in their papers. |
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| This corpus aims to provide a free public dataset for the pronunciation scoring task. |
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| 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. |
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| Five experts made the scores. To avoid subjective bias, each expert scores independently under the same metric. |
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| ## The scoring metric |
| The experts score at three levels: phoneme-level, word-level, and sentence-level. |
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| ### Phoneme level |
| Score the pronunciation goodness of each phoneme within the words. |
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| Score range: 0-2 |
| * 2: pronunciation is correct |
| * 1: pronunciation is right but has a heavy accent |
| * 0: pronunciation is incorrect or missed |
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| ### Word level |
| Score the accuracy and stress of each word's pronunciation. |
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| #### 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 |
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| #### Stress |
| Score range: {5, 10} |
| * 10: The stress is correct, or this is a mono-syllable word |
| * 5: The stress is wrong |
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| ### Sentence level |
| Score the accuracy, fluency, completeness and prosodic at the sentence level. |
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| #### 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 |
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| #### Completeness |
| Score range: 0.0 - 1.0 |
| The percentage of the words with good pronunciation. |
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| #### 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 |
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| #### 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 |
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| ## Data structure |
| The following tree shows the file structure of this corpus: |
| ``` |
| ├── 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 |
| │ ├── ... |
| ├── SPEAKER0003 |
| │ ├── 000030012.WAV |
| │ ├── 000030024.WAV |
| │ ├── 000030040.WAV |
| │ ├── ... |
| └── SPEAKER0005 |
| ├── 000050003.WAV |
| ├── 000050010.WAV |
| ├── 000050024.WAV |
| ├── ... |
| ``` |
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| There are two datasets: `train` and `test`, and both are in Kaldi's data directory style. |
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| The scores are stored in `scores.json`. Here is an example: |
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| ``` |
| { |
| "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] |
| } |
| ] |
| }, |
| ... |
| } |
| ``` |
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| 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. |
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| 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" |
| } |
| ], |
| }, |
| ... |
| } |
| ``` |
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| In `scores-detail.json`, the phoneme-level scores are notated in the following convenient notation: |
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| * 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 |
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| 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. |
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