{"url":"/dataset/nemo-1","name":"nEMO","full_name":null,"description_markdown":"## Overview\r\n\r\nnEMO 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).\r\n\r\nThe dataset is available on [Hugging Face](https://huggingface.co/datasets/amu-cai/nEMO) and [GitHub](https://github.com/amu-cai/nEMO).\r\n\r\n## Data Fields\r\n\r\n- `file_id` - filename, i.e. `{speaker_id}_{emotion}_{sentence_id}`,\r\n\r\n- `audio` (audio) - dictionary containing audio array, path and sampling rate (available when accessed via datasets library),\r\n\r\n- `emotion` - label corresponding to emotional state,\r\n\r\n- `raw_text` - original (orthographic) transcription of the audio,\r\n\r\n- `normalized_text` - normalized transcription of the audio,\r\n\r\n- `speaker_id` - id of speaker,\r\n\r\n- `gender` - gender of the speaker,\r\n\r\n- `age` - age of the speaker.\r\n\r\n## Usage\r\n\r\nThe nEMO dataset can be loaded and processed using the datasets library:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nnemo = load_dataset(\"amu-cai/nEMO\", split=\"train\")\r\n```\r\n\r\nTo work with the nEMO dataset on GitHub, you may clone the repository and access the files directly within the `samples` folder. Corresponding metadata can be found in the `data.tsv` file.\r\n\r\nThe 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.\r\n\r\n## Supported Tasks\r\n\r\n- **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.\r\n- **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.\r\n- **Text-to-Speech:** The dataset contains emotional audio recordings with transcriptions, which can be valuable for developing TTS systems that produce emotionally expressive speech.\r\n\r\n## Additional Information\r\n\r\n### Licensing Information\r\n\r\nThe dataset is available under the Creative Commons license (CC BY-NC-SA 4.0).\r\n\r\n### Citation Information\r\n\r\nYou can access the nEMO paper at [arXiv](https://arxiv.org/abs/2404.06292). Please cite the paper when referencing the nEMO dataset as:\r\n\r\n```\r\n@misc{christop2024nemo,\r\n    title={nEMO: Dataset of Emotional Speech in Polish}, \r\n    author={Iwona Christop},\r\n    year={2024},\r\n    eprint={2404.06292},\r\n    archivePrefix={arXiv},\r\n    primaryClass={cs.CL}\r\n}\r\n```\r\n\r\n### Contributions\r\n\r\nThanks to [@iwonachristop](https://github.com/iwona-christop) for adding this dataset.","description_withheld":null,"homepage":"https://huggingface.co/datasets/amu-cai/nEMO","introduced_date":"2024-04-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/nemo-dataset-of-emotional-speech-in-polish","title":"nEMO: Dataset of Emotional Speech in Polish","first_author":"Iwona Christop","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://github.com/amu-cai/nEMO/blob/main/LICENSE"},"modalities":[{"name":"Audio","url":"/datasets/modality/audio"},{"name":"Speech","url":"/datasets/modality/speech"}],"tasks":[{"name":"Emotion Recognition","url":"/task/emotion-recognition","datasets_with_task":"/datasets/task/emotion-recognition"},{"name":"Audio Classification","url":"/task/audio-classification","datasets_with_task":"/datasets/task/audio-classification"},{"name":"Speech Emotion Recognition","url":"/task/speech-emotion-recognition","datasets_with_task":"/datasets/task/speech-emotion-recognition"},{"name":"Emotion Classification","url":"/task/emotion-classification","datasets_with_task":"/datasets/task/emotion-classification"},{"name":"Audio Emotion Recognition","url":"/task/audio-emotion-recognition","datasets_with_task":"/datasets/task/audio-emotion-recognition"}],"languages":[{"name":"Polish","url":"/datasets/language/polish"}],"variants":["nEMO"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}