README.md

---
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    path: data/train-*
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---

# ExpressiveSpeech Dataset

[**Project Webpage**](https://freedomintelligence.github.io/ExpressiveSpeech/)

[**中文版 (Chinese Version)**](./README_zh.md)

## About The Dataset

**ExpressiveSpeech** is a high-quality, **expressive**, and **bilingual** (Chinese-English) speech dataset created to address the common lack of consistent vocal expressiveness in existing dialogue datasets.

This dataset is meticulously curated from five renowned open-source emotional dialogue datasets: Expresso, NCSSD, M3ED, MultiDialog, and IEMOCAP. Through a rigorous processing and selection pipeline, ExpressiveSpeech ensures that every utterance meets high standards for both acoustic quality and expressive richness. It is designed for tasks in expressive Speech-to-Speech (S2S), Text-to-Speech (TTS), voice conversion, speech emotion recognition, and other fields requiring high-fidelity, emotionally resonant audio.

## Key Features

- **High Expressiveness**: Achieves a significantly high average expressiveness score of **80.2** by **DeEAR**, far surpassing the original source datasets.
- **Bilingual Content**: Contains a balanced mix of Chinese and English speech, with a language ratio close to **1:1**.
- **Substantial Scale**: Comprises approximately **14,000 utterances**, totaling **51 hours** of audio.
- **Rich Metadata**: Includes ASR-generated text transcriptions, expressiveness scores, and source information for each utterance.

## Dataset Statistics

| Metric | Value |
| :--- | :--- |
| Total Utterances | ~14,000 |
| Total Duration | ~51 hours |
| Languages | Chinese, English |
| Language Ratio (CN:EN) | Approx. 1:1 |
| Sampling Rate | 16kHz |
| Avg. Expressiveness Score (DeEAR) | 80.2 |

## Our Expressiveness Scoring Tool: DeEAR

The high expressiveness of this dataset was achieved using our screening tool, **DeEAR**. If you need to build larger batches of high-expressiveness data yourself, you are welcome to use this tool. You can find it on our [GitHub](https://github.com/FreedomIntelligence/ExpressiveSpeech).



## Data Format

The dataset is organized as follows:

```
ExpressiveSpeech/
├── audio/
│   ├── M3ED
│   │    ├── audio_00001.wav
│   │    └── ...
│   ├── NCSSD
│   ├── IEMOCAP
│   ├── MultiDialog
│   └── Expresso
└── metadata.jsonl
```

- **`metadata.jsonl`**: A jsonl file containing detailed information for each utterance. The metadata includes:
  - `audio_path`: The relative path to the audio file.
  - `value`: The ASR-generated text transcription.
  - `emotion`: Emotion labels from the original datasets.
  - `expressiveness_scores`: The expressiveness score from the **DeEAR** model.

### JSONL Files Example

Each JSONL line contains a `conversations` field with an array of utterances.  
Example:

```json
{"conversations": [{"No": 9, "from": "user", "value": "Yeah.", "emotion": "happy", "length": 2.027, "score_arousal": 0.9931480884552002, "score_prosody": 0.6800634264945984, "score_nature": 0.9687601923942566, "score_expressive": 0.9892677664756775, "audio-path": "audios/Expresso/splitted_conversation/ex04-ex01/laughing/ex04-ex01_laughing_001/009_speaker1_53s_55s.wav"}, {"No": 10, "from": "assistant", "value": "What was the reason, what was the, why couldn't I get there, ah I forget.", "emotion": "happy", "length": 3.753, "score_arousal": 0.9555678963661194, "score_prosody": 0.6498672962188721, "score_nature": 1.030701756477356, "score_expressive": 0.9965837001800537, "audio-path": "audios/Expresso/splitted_conversation/ex04-ex01/laughing/ex04-ex01_laughing_001/010_speaker2_55s_59s.wav"}]}
{"conversations": [{"No": 10, "from": "user", "value": "What was the reason, what was the, why couldn't I get there, ah I forget.", "emotion": "happy", "length": 3.753, "score_arousal": 0.9555678963661194, "score_prosody": 0.6498672962188721, "score_nature": 1.030701756477356, "score_expressive": 0.9965837001800537, "audio-path": "audios/Expresso/splitted_conversation/ex04-ex01/laughing/ex04-ex01_laughing_001/010_speaker2_55s_59s.wav"}, {"No": 11, "from": "assistant", "value": "Because genie really had to go and and to the bathroom and she couldn't find a place to do it and so she when they put the tent on it it was it was a bad mess and they shouldn't have done that.", "emotion": "happy", "length": 10.649, "score_arousal": 0.976757287979126, "score_prosody": 0.7951533794403076, "score_nature": 0.9789049625396729, "score_expressive": 0.919080913066864, "audio-path": "audios/Expresso/splitted_conversation/ex04-ex01/laughing/ex04-ex01_laughing_001/011_speaker1_58s_69s.wav"}]}
```

*Note*: Some source datasets applied VAD, which could split a single utterance into multiple segments. To maintain conversational integrity, we applied rules to merge such segments back into complete utterances.


## License

In line with the non-commercial restrictions of its source datasets, the ExpressiveSpeech dataset is released under the CC BY-NC-SA 4.0 license.

You can view the full license [here](https://creativecommons.org/licenses/by-nc-sa/4.0/).

## Citation

If you use this dataset in your research, please cite our paper:

```bibtex
@article{lin2025decoding,
  title={Decoding the Ear: A Framework for Objectifying Expressiveness from Human Preference Through Efficient Alignment},
  author={Lin, Zhiyu and Yang, Jingwen and Zhao, Jiale and Liu, Meng and Li, Sunzhu and Wang, Benyou},
  journal={arXiv preprint arXiv:2510.20513},
  year={2025}
}
```