Datasets:

amu-cai
/

nEMO

	---
	language:
	- pl
	license: cc-by-nc-sa-4.0
	multilinguality: monolingual
	size_categories:
	- 1K<n<10K
	task_categories:
	- audio-classification
	- automatic-speech-recognition
	- text-to-speech
	task_ids:
	- audio-emotion-recognition
	- audio-language-identification
	- sentiment-analysis
	- speaker-identification
	pretty_name: nEMO
	dataset_info:
	features:
	- name: file_id
	dtype: string
	- name: audio
	dtype: audio
	- name: emotion
	dtype: string
	- name: raw_text
	dtype: string
	- name: normalized_text
	dtype: string
	- name: speaker_id
	dtype: string
	- name: gender
	dtype: string
	- name: age
	dtype: int32
	splits:
	- name: train
	num_bytes: 531802170.875
	num_examples: 4481
	download_size: 531224302
	dataset_size: 531802170.875
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	---
	# nEMO: Dataset of Emotional Speech in Polish

	## Dataset Description

	nEMO is a simulated dataset of emotional speech in the Polish language. The corpus contains over 3 hours of samples recorded with the participation of nine actors portraying six emotional states: anger, fear, happiness, sadness, surprise, and a neutral state. The text material used was carefully selected to represent the phonetics of the Polish language. The corpus is available for free under the Creative Commons license (CC BY-NC-SA 4.0).

	### Example Usage

	The nEMO dataset can be loaded and processed using the datasets library:

	```python
	from datasets import load_dataset

	nemo = load_dataset("amu-cai/nEMO", split="train")
	```

	### Supported Tasks

	- `audio-classification`: This dataset was mainly created for the task of speech emotion recognition. Each recording is labeled with one of six emotional states (anger, fear, happiness, sadness, surprised, and neutral). Additionally, each sample is labeled with speaker id and speaker gender. Because of that, the dataset can also be used for different audio classification tasks.
	- `automatic-speech-recognition`: The dataset includes orthographic and normalized transcriptions for each audio recording, making it a useful resource for automatic speech recognition (ASR) tasks. The sentences were carefully selected to cover a wide range of phonemes in the Polish language.
	- `text-to-speech`: The dataset contains emotional audio recordings with transcriptions, which can be valuable for developing TTS systems that produce emotionally expressive speech.

	### Languages

	nEMO contains audio and transcription in Polish language.

	## Dataset Structure

	### Data Instances

	```python
	{
	'file_id': 'WR0_surprised_64',
	'audio': {
	'path': None,
	'array': array([ 6.10351562e-05, -3.05175781e-05, -3.05175781e-05, ...,
	6.10351562e-05, -1.22070312e-04, 1.83105469e-04]),
	'sampling_rate': 24000
	},
	'emotion': 'surprised',
	'raw_text': 'Ucho wykrywa dźwięki o różnej częstotliwości.',
	'normalized_text': 'ucho wykrywa dźwięki o różnej częstotliwości',
	'speaker_id': 'WR0',
	'gender': 'male',
	'age': 23
	}
	```

	### Data Fields

	- `file_id` (string) - id of file, i.e. `{speaker_id}_{emotion}_{sentence_id}`,
	- `audio` (audio) - dictionary containing audio array, path and sampling rate,
	- `emotion` (string) - label corresponding to emotional state,
	- `raw_text` (string) - original (orthographic) transcription of the audio,
	- `normalized_text` (string) - normalized transcription of the audio,
	- `speaker_id` (string) - id of speaker,
	- `gender` (string) - gender of the speaker,
	- `age` (int32) - age of the speaker.

	### Data Splits

	The nEMO dataset is provided as a whole, without predefined training and test splits. This allows researchers and developers flexibility in creating their splits based on the specific needs.

	\| Emotion \| Number of samples \|
	\| --------- \| ----------------- \|
	\| anger \| 749 \|
	\| fear \| 736 \|
	\| happiness \| 749 \|
	\| neutral \| 809 \|
	\| sadness \| 769 \|
	\| surprised \| 669 \|
	\| Total \| 4,481 \|

	## Additional Information

	### Licensing Information

	The dataset is available under the Creative Commons license (CC BY-NC-SA 4.0).

	### Citation Information

	You can access the nEMO paper at [ACL Anthology](https://aclanthology.org/2024.lrec-main.1059/). Please cite the paper when referencing the nEMO dataset as:

	```
	@inproceedings{christop-2024-nemo-dataset,
	title = "n{EMO}: Dataset of Emotional Speech in {P}olish",
	author = "Christop, Iwona",
	editor = "Calzolari, Nicoletta and
	Kan, Min-Yen and
	Hoste, Veronique and
	Lenci, Alessandro and
	Sakti, Sakriani and
	Xue, Nianwen",
	booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
	month = may,
	year = "2024",
	address = "Torino, Italia",
	publisher = "ELRA and ICCL",
	url = "https://aclanthology.org/2024.lrec-main.1059",
	pages = "12111--12116",
	abstract = "Speech emotion recognition has become increasingly important in recent years due to its potential applications in healthcare, customer service, and personalization of dialogue systems. However, a major issue in this field is the lack of datasets that adequately represent basic emotional states across various language families. As datasets covering Slavic languages are rare, there is a need to address this research gap. This paper presents the development of nEMO, a novel corpus of emotional speech in Polish. The dataset comprises over 3 hours of samples recorded with the participation of nine actors portraying six emotional states: anger, fear, happiness, sadness, surprise, and a neutral state. The text material used was carefully selected to represent the phonetics of the Polish language adequately. The corpus is freely available under the terms of a Creative Commons license (CC BY-NC-SA 4.0).",
	}
	```

	### Contributions

	Thanks to [@iwonachristop](https://github.com/iwona-christop) for adding this dataset.