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@@ -58,3 +58,132 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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speechocean762/.gitignore

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+.DS_Store
+work/

speechocean762/README.md

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+# 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.
+However, there is no public dataset for this task. Researchers use their private datasets in their papers.
+
+This corpus aims to provide a free public dataset for the pronunciation scoring task.
+
+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:
+```
+├── 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
+        ├── ...
+```
+
+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]
+            }
+        ]
+    },
+    ...
+}
+```
+
+
+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.

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