---
dataset_info:
  features:
  - name: talk_id
    dtype: string
  - name: segment_pos
    dtype: uint16
  - name: title
    dtype: string
  - name: speaker_id
    dtype: string
  - name: transcription
    dtype: string
  - name: audio
    dtype: audio
  splits:
  - name: test.2020
    num_bytes: 1672692975.652
    num_examples: 4017
  - name: test.2022
    num_bytes: 2278055091.504
    num_examples: 9738
  download_size: 4110027783
  dataset_size: 3950748067.156
configs:
- config_name: default
  data_files:
  - split: test.2020
    path: data/test.2020-*
  - split: test.2022
    path: data/test.2022-*
language:
- en
license: cc-by-nc-nd-4.0
tags:
- audio
- automatic-speech-recognition
- speech
- particle-physics
- cern
- narrow-domain
task_categories:
- automatic-speech-recognition
pretty_name: LHCP-ASR Segments
size_categories:
- 10K<n<100K
---

# LHCP-ASR Segments

This dataset is a segment-level distribution derived from [mllp/LHCP-ASR](https://huggingface.co/datasets/mllp/LHCP-ASR) (and the original [LHCP-ASR repository](https://github.com/mllpresearch/LHCP-ASR)), an English speech corpus for narrow-domain ASR benchmarking in high-energy particle physics.

Unlike previous versions, this repository provides audio directly at the segment level (<30 seconds each) for evaluation, adds talk-level metadata and cleans up transcription tags.

## Differences from `mllp/LHCP-ASR`

1. **No subsets:** Directly formatted as segments (<30s audio chunks). There is no `longform` configuration.
2. **Evaluation splits:** Specifically contains the test partitions:
   - `test.2020`
   - `test.2022`
3. **Text normalization:** Occurrences of `<UNK>` (used in manual transcriptions to mark unintelligible speech) have been removed.
4. **Metadata:** Each sample now contains talk-level metadata fields (`talk_id`, `segment_pos`, `title` and `speaker_id`).

### Metadata fields

* `talk_id` (*str*): Identifier for the talk to which the segment belongs.
* `segment_pos` (*int*): Sequential position of the segment within the talk.
* `title` (*str*): Title of the plenary talk.
* `speaker_id` (*str*): Identifier of the primary speaker of the talk. This field refers to the assigned presenter of the talk, not strictly the voice heard in every segment. Segments may contain speech from session chairs or Q&A audience members.

---

## Usage

You can load the dataset directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("mllp/LHCP-ASR-segments")

# Or load a specific test split
test_2020 = load_dataset("mllp/LHCP-ASR-segments", split="test.2020")
test_2022 = load_dataset("mllp/LHCP-ASR-segments", split="test.2022")

```

---

## Citation

If you use this dataset, please cite the original work:

```bibtex
@inproceedings{santamariajorda25_interspeech,
  title     = {{LHCP-ASR: An English Speech Corpus of High-Energy Particle Physics Talks for Narrow-Domain ASR Benchmarking}},
  author    = {Jaume Santamaría-Jordà and Pablo Segovia-Martínez and Gonçal V. {Garcés Díaz-Munío} and Joan Albert Silvestre-Cerdà and Adrià Giménez and Rubén {Gaspar Aparicio} and René {Fernández Sánchez} and Jorge Civera and Albert Sanchis and Alfons Juan},
  year      = {2025},
  booktitle = {{Interspeech 2025}},
  pages     = {4033--4037},
  doi       = {10.21437/Interspeech.2025-2630},
  issn      = {2958-1796},
}
```

For more details on the original dataset, visit [https://github.com/mllpresearch/LHCP-ASR](https://github.com/mllpresearch/LHCP-ASR).

---

## Legal disclaimer

Speech and text data were provided by the [European Organization 
for Nuclear Research (CERN)](https://home.cern/) under PO OV9177345.
The following disclaimers are those available in the [CERN Document Server (CDS)](https://cds.cern.ch/) repository on May 30th, 2025:

#### CERN Document Server - Terms and Conditions
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---

## License

This dataset is licenced under CC-BY-NC-ND 4.0. To view a copy of this licence, visit https://creativecommons.org/licenses/by-nc-nd/4.0/