---
license: cc0-1.0
dataset_info:
  features:
  - name: filename
    dtype: string
  - name: gender
    dtype: string
  - name: age
    dtype: string
  - name: language
    dtype: string
  - name: text
    dtype: string
  - name: audio
    dtype: audio
  splits:
  - name: train
    num_bytes: 1047469197.184
    num_examples: 5242
  download_size: 879436814
  dataset_size: 1047469197.184
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# CommonPhone-SE

<!-- Provide a quick summary of the dataset. -->

Multilingual, age and gender balanced subset for speech enhancement benchmark. 

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

Commonphone-SE is a benchmark dataset derived from Commonphone. It contains audio samples from 7 languages in the age range from 18 to 80. It aims to 
provide a speaker diverse dataset to benchmark speech enhancement algorithms in real world conditions. 

- **Curated by:** LangTech Lab members from the speech team. 
- **Language(s) (NLP):** CA, DE, EN, IT, FR, RU, ES
- **License:** cc0-1.0

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

CommonPhone-SE is a subset of CommonPhone. 

- **Repository:** https://zenodo.org/records/5846137
- **Paper :** https://arxiv.org/abs/2201.05912

### Languages

Catalan(CA), Deutsch(DE), English(EN), Italian(IT), French(FR), Russian(RU), Spanish(ES) 

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

The goal of this dataset is to evaluate the generalization capabilities of speech enhancement models in a real world multilingual and diverse dataset.



## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset consists of a single split, providing audios, transcriptions and demographic information

```
Dataset({
    features: ['filename', 'gender', 'age', 'language', 'text', 'audio'],
    num_rows: 5242
})
```

Each data point is structured as:

```
{'filename': 'common_voice_ca_31498257',
 'gender': 'female',
 'age': 'fifties',
 'language': 'ca',
 'text': 'lieutenant monroe va resultar ferit durant la batalla i va servir posteriorment al congrés',
 'audio': {'path': 'Commonphone-SE/common_voice_ca_31498257.wav',
 'array': array([0., 0., 0., ..., 0., 0., 0.]),
 'sampling_rate': 16000}}
```

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The sampling rationale was to select audios that remain difficult for state of the art enhancement models, both in terms of speech quality metrics and content preservation, hence, we selected the worst 40 examples w.r.t. to UTMOS, SCOREQ and WIL per each language, age band and gender. Finally, the duplicates were dropped to arrive at a final evaluation dataset of 8.24 hours.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
Crowdsourced audios recorded by volunters for CommonVoice that were selected in the CommonPhone dataset.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

Common Phone is maintained and distributed by speech researchers at the Pattern Recognition Lab of Friedrich-Alexander-University Erlangen-Nuremberg (FAU)


#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

Like for Common Voice, you must not make any attempt to identify speakers that contributed to CommonPhone-SE.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The dataset was built trying to mitigate the bias on gender and age variables, however, it can still be biased towards the degradations found in the commonvoice corpus. Althoug this dataset has a lot of diversity the style is only reading speech. 


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

If you use the dataset please cite

**BibTeX:**
```
@inproceedings{giraldo25_interspeech,
  title     = {{Evaluating Speech Enhancement Performance Across Demographics and Language}},
  author    = {{Jose Giraldo and Alex Peiró-Lilja and Carme Armentano-Oller and Rodolfo Zevallos and Cristina España-Bonet}},
  year      = {{2025}},
  booktitle = {{Interspeech 2025}},
  pages     = {{1353--1357}},
  doi       = {{10.21437/Interspeech.2025-1760}},
  issn      = {{2958-1796}},
}
```

## Dataset Card Authors


## Funding
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/) and also by the Ministerio para la Transformación
Digital y de la Función Pública and Plan de Recuperación,Transformación y Resiliencia - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215337 

## Dataset Card Contact
langtech@bsc.es