---
annotations_creators: [manual]
language_creators: [found]
language: [it]
license: cc-by-4.0
multilinguality: [monolingual]
pretty_name: AI4SER
tags: [speech-emotion-recognition, audio, emotions, italian, wav]
task_categories: [audio-classification]
task_ids: [audio-language-identification]
dataset_info:
  features:
    - name: file
      dtype: audio
    - name: emotion
      dtype: string
    - name: speaker_id
      dtype: string
    - name: sentence_id
      dtype: string
---

# AI4SER: Artificial Intelligence for Speech Emotion Recognition

**AI4SER** is an emotional speech dataset designed for **Speech Emotion Recognition (SER)** tasks.  
It contains utterances in **Italian**, recorded by multiple speakers, and annotated with **7 emotional categories**.

The dataset has been created following **Open Science** and **FAIR data principles**, and it is publicly available under a **CC BY 4.0** license.

---

## 🧠 Emotion Labels

The emotional category of each utterance is encoded in the **filename** using a **3-letter prefix**:

| Code | Emotion     |
|------|-------------|
| dis  | Disgust     |
| hap  | Happiness   |
| fea  | Fear        |
| ang  | Anger       |
| sur  | Surprise    |
| sad  | Sadness     |
| neu  | Neutral     |

---

## 🗂️ File Naming Convention

Each `.wav` file is named using the following format:

`<emotion_code>_<phrase_id>_<speaker_id>.wav`

For example:

- `hap_03_07.wav`: Happiness, phrase 03, speaker 07  
- `sad_10_02.wav`: Sadness, phrase 10, speaker 02

---

## 📁 Directory Structure

The dataset is organized as follows:
<pre> 
AI4SER/
├── 01/ # Speaker 01
│ ├── dis_01_01.wav
│ ├── hap_02_01.wav
│ └── ...
├── 02/ # Speaker 02
│ ├── ang_03_02.wav
│ ├── neu_04_02.wav
│ └── ...
├── ...
</pre>

Each subfolder corresponds to a **speaker ID**.

---

## 📐 Technical Details

- **Format**: 16-bit PCM WAV
- **Sampling Rate**: 44,100 Hz
- **Language**: Italian
- **Utterances per speaker**: 70 (7 emotions × 10 phrases)
- **Duration**: Variable

---

## 🏷️ Annotations 

In addition to the main dataset splits, we provide four supplementary CSV files (values are separated by `;`):

| File name                | Description                                                                                          |
|--------------------------|------------------------------------------------------------------------------------------------------|
| `EMOTIONS-HIT-RATIO.csv` | Fraction of listeners who identified each audio file with one of the 7 considered discrete emotions. |
| `VALENCE.csv`         | Mean value of the **Valence** parameter assigned by listeners for each audio file.                |
| `DOMINANCE.csv`          | Mean value of the **Dominance** parameter assigned by listeners for each audio file.                 |
| `AROUSAL.csv`            | Mean value of the **Arousal** parameter assigned by listeners for each audio file.                   |

### Rating scales

Listeners annotated **valence, dominance, and arousal** using discrete attributes mapped onto the following numerical scale:

| Value | Valence interpretation   | Dominance interpretation | Arousal interpretation  |
|-------|--------------------------|--------------------------|-------------------------|
| -3    | Very negative            | Very submissive          | Very calm               |
| -2    | Negative                 | Submissive               | Calm                    |
| -1    | Slightly negative        | Slightly submissive      | Slightly calm           |
| 0     | Neutral                  | Neutral                  | Neutral                 |
| +1    | Slightly positive        | Slightly dominant        | Slightly intense        |
| +2    | Positive                 | Dominant                 | Intense                 |
| +3    | Very positive            | Very dominant            | Very intense            |

---

## 📖 Sentences (Italian ↔ English)

The dataset includes 10 reference sentences, provided in **Italian**, covering the 7 emotional categories plus neutral and nonsense phrases.

|Code| Italian sentence                                | English translation                           | Emotion   |
|----|-------------------------------------------------|-----------------------------------------------|-----------|
|01  | La giacca giace sul frigorifero                 | The jacket lies on the refrigerator           | Nonsense  |
|02  | Il cane sta camminando nella mela               | The dog is walking inside the apple           | Nonsense  |
|03  | Cosa farà domani?                               | What will he/she do tomorrow?                 | Neutral   |
|04  | Lo hanno appena trasferito                      | He/She has just been transferred              | Neutral   |
|05  | Devi portare con te questo peso                 | You have to carry this burden with you        | Sadness   |
|06  | Ho scoperto questo libro, è fantastico          | I discovered this book, it's fantastic        | Surprise  |
|07  | Come è potuto succedere tutto questo?           | How could all this have happened?             | Anger     |
|08  | Ho vinto un premio!                             | I won a prize!                                | Happiness |
|09  | Entrando nella casa c’era puzza                 | There was a stench when entering the house    | Disgust   |
|10  | Tra sette ore verrà il momento                  | The time will come in seven hours             | Fear      |

---

## 📄 License

This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.

---

## 📚 Citation & Funding Acknowledgement

This dataset was developed by the **Digital Signal Processing Lab** – Department of Engineering – University of Messina.

> This work was supported by the European Union – Next Generation EU under the Italian National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.3, CUP C49J24000240004, partnership on “Telecommunications of the Future” (PE00000001 – program “RESTART”).

If you use this dataset in your research, please cite it appropriately

Salvatore Serrano, Marica Amadeo, Luca Patanè, Marco Scarpa, Carmela Mento, Silvia Carbone and Giulia Esposito, 
**"AI4SER: An Italian Speech Emotion Dataset for Human-Centered Affective Applications"**, 
24th IEEE International Conference on Pervasive Computing and Communications (PerCom 2026) Workshops, Pisa, Italy, 2026

---

## 📦 Usage with Hugging Face 🤗 Datasets

You can load the dataset using:

```python
from datasets import load_dataset

dataset = load_dataset("sirsalvo72/AI4SER")