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--- |
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language: |
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- en |
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license: apache-2.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- audio-classification |
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- automatic-speech-recognition |
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pretty_name: ' ' |
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tags: |
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- pronunciation-scoring |
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- L1-Mandarin |
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- L2-English |
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dataset_info: |
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features: |
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- name: spk |
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dtype: string |
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- name: age |
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dtype: string |
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- name: gender |
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dtype: string |
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- name: utt_name |
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dtype: string |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: utt_text |
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dtype: string |
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- name: utt_accuracy |
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dtype: int64 |
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- name: utt_completeness |
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dtype: float64 |
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- name: utt_fluency |
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dtype: int64 |
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- name: utt_prosodic |
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dtype: int64 |
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- name: utt_total |
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dtype: int64 |
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- name: words |
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sequence: string |
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- name: words_accuracy |
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sequence: int64 |
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- name: words_stress |
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sequence: int64 |
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- name: words_total |
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sequence: int64 |
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- name: phones |
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sequence: |
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sequence: string |
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- name: phones_godness |
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sequence: |
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sequence: float64 |
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- name: duration |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 333075617.5 |
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num_examples: 2500 |
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- name: test |
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num_bytes: 311790040.5 |
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num_examples: 2500 |
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download_size: 611757634 |
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dataset_size: 644865658.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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# speechocean762: A non-native English corpus for pronunciation scoring task |
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## How to use? |
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you can load data using |
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```py |
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speechocean762_dataset = load_dataset('seba3y/speechocean762') |
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``` |
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```py |
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>> speechocean762_dataset |
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DatasetDict({ |
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train: Dataset({ |
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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'], |
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num_rows: 2500 |
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}) |
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test: Dataset({ |
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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'], |
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num_rows: 2500 |
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}) |
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}) |
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``` |
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Features are ordered as following: |
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1- Demographic featurs: `'spk', 'age', 'gender', 'utt_name'` |
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2- Sentence-level featurs: `'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total'` |
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3- Word-level featurs: `'words', 'words_accuracy', 'words_stress', 'words_total'` |
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4- Phoneme-level featurs: `'phones', 'phones_godness'` |
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```py |
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>> speechocean762_dataset['train'][0] |
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``` |
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```py |
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{'spk': '0001', |
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'age': 6, |
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'gender': 'm', |
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'utt_name': '000010011', |
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'audio': {'path': '/content/speechocean762/WAVE/SPEAKER0001/000010011.WAV', |
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'array': array([-9.46044922e-04, -2.38037109e-03, -1.31225586e-03, ..., |
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-9.15527344e-05, 3.05175781e-04, -2.44140625e-04]), |
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'sampling_rate': 16000}, |
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'utt_text': 'WE CALL IT BEAR', |
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'utt_accuracy': 8, |
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'utt_completeness': 10.0, |
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'utt_fluency': 9, |
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'utt_prosodic': 9, |
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'utt_total': 8, |
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'words': "['WE', 'CALL', 'IT', 'BEAR']", |
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'words_accuracy': '[10, 10, 10, 6]', |
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'words_stress': '[10, 10, 10, 10]', |
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'words_total': '[10, 10, 10, 6]', |
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'phones': "[['W', 'IY0'], ['K', 'AO0', 'L'], ['IH0', 'T'], ['B', 'EH0', 'R']]", |
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'phones_godness': '[[2.0, 2.0], [2.0, 1.8, 1.8], [2.0, 2.0], [2.0, 1.0, 1.0]]'} |
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``` |
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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 |
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## Introduction |
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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. |
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This corpus aims to provide a free public dataset for the pronunciation scoring task. |
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Key features: |
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* It is available for free download for both commercial and non-commercial purposes. |
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* The speaker variety encompasses young children and adults. |
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* The manual annotations are in multiple aspects at sentence-level, word-level and phoneme-level. |
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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 |
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The experts score at three levels: phoneme-level, word-level, and sentence-level. |
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### Phoneme level |
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Score the pronunciation goodness of each phoneme within the words. |
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Score range: 0-2 |
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* 2: pronunciation is correct |
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* 1: pronunciation is right but has a heavy accent |
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* 0: pronunciation is incorrect or missed |
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### Word level |
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Score the accuracy and stress of each word's pronunciation. |
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#### Accuracy |
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Score range: 0 - 10 |
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* 10: The pronunciation of the word is perfect |
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* 7-9: Most phones in this word are pronounced correctly but have accents |
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* 4-6: Less than 30% of phones in this word are wrongly pronounced |
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* 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" |
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* 1: The pronunciation is hard to distinguish |
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* 0: no voice |
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#### Stress |
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Score range: {5, 10} |
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* 10: The stress is correct, or this is a mono-syllable word |
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* 5: The stress is wrong |
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### Sentence level |
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Score the accuracy, fluency, completeness and prosodic at the sentence level. |
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#### Accuracy |
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Score range: 0 - 10 |
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* 9-10: The overall pronunciation of the sentence is excellent, with accurate phonology and no obvious pronunciation mistakes |
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* 7-8: The overall pronunciation of the sentence is good, with a few pronunciation mistakes |
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* 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 |
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* 3-4: Poor, clumsy and rigid pronunciation of the sentence as a whole, with serious pronunciation mistakes |
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* 0-2: Extremely poor pronunciation and only one or two words are recognizable |
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#### Completeness |
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Score range: 0.0 - 1.0 |
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The percentage of the words with good pronunciation. |
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#### Fluency |
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Score range: 0 - 10 |
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* 8-10: Fluent without noticeable pauses or stammering |
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* 6-7: Fluent in general, with a few pauses, repetition, and stammering |
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* 4-5: the speech is a little influent, with many pauses, repetition, and stammering |
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* 0-3: intermittent, very influent speech, with lots of pauses, repetition, and stammering |
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#### Prosodic |
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Score range: 0 - 10 |
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* 9-10: Correct intonation at a stable speaking speed, speak with cadence, and can speak like a native |
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* 7-8: Nearly correct intonation at a stable speaking speed, nearly smooth and coherent, but with little stammering and few pauses |
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* 5-6: Unstable speech speed, many stammering and pauses with a poor sense of rhythm |
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* 3-4: Unstable speech speed, speak too fast or too slow, without the sense of rhythm |
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* 0-2: Poor intonation and lots of stammering and pauses, unable to read a complete sentence |
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## Data structure |
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The following tree shows the file structure of this corpus on [github](https://github.com/jimbozhang/speechocean762): |
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``` |
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βββ scores.json |
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βββ scores-detail.json |
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βββ train |
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β βββ spk2age |
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β βββ spk2gender |
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β βββ spk2utt |
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β βββ text |
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β βββ utt2spk |
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β βββ wav.scp |
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βββ test |
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β βββ spk2age |
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β βββ spk2gender |
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β βββ spk2utt |
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β βββ text |
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β βββ utt2spk |
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β βββ wav.scp |
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βββ WAVE |
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βββ SPEAKER0001 |
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β βββ 000010011.WAV |
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β βββ 000010035.WAV |
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β βββ ... |
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β βββ 000010173.WAV |
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βββ SPEAKER0003 |
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β βββ 000030012.WAV |
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β βββ 000030024.WAV |
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β βββ ... |
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β βββ 000030175.WAV |
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βββ SPEAKER0005 |
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βββ 000050003.WAV |
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βββ 000050010.WAV |
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βββ ... |
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βββ 000050175.WAV |
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``` |
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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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``` |
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{ |
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"000010011": { # utt-id |
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"text": "WE CALL IT BEAR", # transcript text |
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"accuracy": 8, # sentence-level accuracy score |
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"completeness": 10.0, # sentence-level completeness score |
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"fluency": 9, # sentence-level fluency score |
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"prosodic": 9, # sentence-level prosodic score |
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"total": 8, # sentence-level total score |
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"words": [ |
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{ |
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"accuracy": 10, # word-level accuracy score |
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"stress": 10, # word-level stress score |
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"total": 10, # word-level total score |
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"text": "WE", # the word text |
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"phones": "W IY0", # phones of the word |
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"phones-accuracy": [2.0, 2.0] # phoneme-level accuracy score |
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}, |
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{ |
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"accuracy": 10, |
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"stress": 10, |
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"total": 10, |
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"text": "CALL", |
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"phones": "K AO0 L", |
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"phones-accuracy": [2.0, 1.8, 1.8] |
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}, |
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{ |
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"accuracy": 10, |
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"stress": 10, |
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"total": 10, |
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"text": "IT", |
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"phones": "IH0 T", |
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"phones-accuracy": [2.0, 2.0] |
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}, |
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{ |
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"accuracy": 6, |
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"stress": 10, |
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"total": 6, |
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"text": "BEAR", |
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"phones": "B EH0 R", |
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"phones-accuracy": [2.0, 1.0, 1.0] |
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} |
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] |
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}, |
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... |
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} |
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``` |
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For the phones with an accuracy score lower than 0.5, an extra "mispronunciations" block indicates which phoneme the current phone was actually pronounced. |
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An example: |
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``` |
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{ |
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"text": "LISA", |
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"accuracy": 5, |
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"phones": ["L", "IY1", "S", "AH0"], |
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"phones-accuracy": [0.4, 2, 2, 1.2], |
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"mispronunciations": [ |
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{ |
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"canonical-phone": "L", |
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"index": 0, |
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"pronounced-phone": "D" |
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} |
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], |
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"stress": 10, |
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"total": 6 |
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} |
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``` |
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The file `scores.json` is processed from `scores-detail.json`. |
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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`: |
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``` |
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{ |
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"000010011": { |
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"text": "WE CALL IT BEAR", |
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"accuracy": [7.0, 9.0, 8.0, 8.0, 9.0], |
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"completeness": [1.0, 1.0, 1.0, 1.0, 1.0], |
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"fluency": [10.0, 9.0, 8.0, 8.0, 10.0], |
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"prosodic": [10.0, 9.0, 7.0, 8.0, 9.0], |
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"total": [7.6, 9.0, 7.9, 8.0, 9.1], |
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"words": [ |
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{ |
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"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"stress": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"total": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"text": "WE", |
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"ref-phones": "W IY0", |
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"phones": ["W IY0", "W IY0", "W IY0", "W IY0", "W IY0"] |
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}, |
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{ |
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"accuracy": [10.0, 8.0, 10.0, 10.0, 8.0], |
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"stress": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"total": [10.0, 8.4, 10.0, 10.0, 8.4], |
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"text": "CALL", |
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"ref-phones": "K AO0 L", |
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"phones": ["K AO0 L", "K {AO0} L", "K AO0 L", "K AO0 L", "K AO0 {L}"], |
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}, |
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{ |
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"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"stress": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"total": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"text": "IT", |
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"ref-phones": "IH0 T", |
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"phones": ["IH0 T", "IH0 T", "IH0 T", "IH0 T", "IH0 T"] |
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}, |
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{ |
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"accuracy": [3.0, 7.0, 10.0, 2.0, 6.0], |
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"stress": [10.0, 10.0, 10.0, 10.0, 10.0], |
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"phones": ["B (EH0) (R)", "B {EH0} {R}", "B EH0 R", "B (EH0) (R)", "B EH0 [L] R"], |
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"total": [4.4, 7.6, 10.0, 3.6, 6.8], |
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"text": "BEAR", |
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"ref-phones": "B EH0 R" |
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} |
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], |
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}, |
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... |
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} |
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``` |
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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 |
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* for score 1, use "{}" symbol |
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* for score 0, use "()" symbol |
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* 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, |
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"B EH [L] R" mean there is an unexpected phone "L" and the other phones are scored 2. |
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## Citation |
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Please cite our paper if you find this work useful: |
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|
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```bibtex |
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@inproceedings{zhang2021speechocean762, |
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title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment}, |
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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}, |
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booktitle={Proc. Interspeech 2021}, |
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year={2021} |
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} |
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``` |