changelinglab/speechocean-l2eval

---
license: cc-by-nc-4.0
arxiv: 2601.14046
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: speaker_id
dtype: string
- name: utt_id
dtype: string
- name: text
dtype: string
- name: accuracy
dtype: int32
- name: completeness
dtype: float32
- name: fluency
dtype: int32
- name: prosodic
dtype: int32
- name: total
dtype: int32
splits:
- name: train
num_bytes: 260979874
num_examples: 2260
- name: val
num_bytes: 37136358
num_examples: 240
- name: test
num_bytes: 288161567
num_examples: 2500
download_size: 610453123
dataset_size: 586277799
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
task_categories:
- automatic-speech-recognition
language:
- en
size_categories:
- 1K<n<10K
---

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

## Dataset Summary

**speechocean762** is an open-source non-native English speech corpus designed for **pronunciation assessment** and **L2 spoken proficiency modeling**.
This Hugging Face version provides **sentence-level audio and expert scores**, organized into standard `train` / `validation` / `test` splits.

All speakers are Mandarin L1 learners of English, spanning both children and adults. Each utterance is evaluated independently by five expert annotators using standardized pronunciation metrics.

This dataset is suitable for:
- pronunciation scoring
- L2 speech assessment
- speech representation learning
- downstream regression or classification tasks

## Dataset Structure

### Splits

The dataset is published with three predefined splits:
- `train` (2260)
- `val` (240)
- `test` (2500)

Splits are **speaker-disjoint** and provided as native Hugging Face splits.

### Features

Each example contains:

| Field | Type | Description |
|--------|------|-------------|
| `audio` | `Audio` | Speech waveform (16 kHz) |
| `speaker_id` | `string` | Speaker identifier |
| `utt_id` | `string` | Utterance identifier |
| `text` | `string` | Prompt sentence |
| `accuracy` | `int` | Sentence-level pronunciation accuracy |
| `completeness` | `float` | Percentage of correctly pronounced words |
| `fluency` | `int` | Sentence-level fluency score |
| `prosodic` | `int` | Sentence-level prosody score |
| `total` | `int` | Overall pronunciation score |

## Scoring Metrics (Sentence level)
All sentence-level scores follow the original speechocean762 definitions.
For detailed descriptions, see:
- **arXiv:** https://arxiv.org/abs/2104.01378
- **Github:** https://github.com/jimbozhang/speechocean762

## Dataset Creation
This Hugging Face dataset is derived from the original speechocean762 corpus and includes:
- sentence-level audio
- sentence-level expert scores
- standardized HF Audio features
- speaker-disjoint train/val/test splits

Word-level and phoneme-level annotations are not included in this version.

**Source Dataset**: https://huggingface.co/datasets/mispeech/speechocean762

## License

The original speechocean762 dataset is released for free use, including commercial and non-commercial purposes, as stated by the original authors.
Users should consult the original repository for full licensing details.

## Citation

If you use this dataset, please cite the original paper:

```bibtex
@inproceedings{zhang2021speechocean762,
title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment},
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},
booktitle={Proc. Interspeech 2021},
year={2021}
}
```

## Acknowledgements

All credit for data collection and annotation belongs to the original speechocean762 authors.
This Hugging Face release focuses on standardized access and reproducibility for modern speech and representation learning pipelines.

You can use this dataset with our benchmarking toolkit at https://github.com/changelinglab/prism
```
@misc{prism2026,
title={PRiSM: Benchmarking Phone Realization in Speech Models},
author={Shikhar Bharadwaj and Chin-Jou Li and Yoonjae Kim and Kwanghee Choi and Eunjung Yeo and Ryan Soh-Eun Shim and Hanyu Zhou and Brendon Boldt and Karen Rosero Jacome and Kalvin Chang and Darsh Agrawal and Keer Xu and Chao-Han Huck Yang and Jian Zhu and Shinji Watanabe and David R. Mortensen},
year={2026},
eprint={2601.14046},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2601.14046},
}
```