README.md

# Speech Recognition Benchmark for Korean Meteorological Experts

This repository provides the dataset accompanying the paper:  
[*Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts*](https://arxiv.org/abs/2410.18444), EMNLP2025 (short, findings).

## Overview
This dataset is created to evaluate **automatic speech recognition (ASR) systems** in the domain of Korean meteorology. Unlike general-purpose speech benchmarks, it emphasizes:

- Frequent use of **specialized meteorological terminology**  
- **Korean linguistic challenges** such as spacing and agglutinative morphology  
- Utterances reflecting **realistic expert queries** to weather information systems  

The dataset enables reliable benchmarking of ASR models for weather-related applications.

## Structure
- **Audio Data**: WAV files containing meteorological queries (commonly sampled at 48kHz, though sampling rate may vary by file).  
- **Transcriptions**: Human-verified transcriptions aligned with the audio.  

### Split
- `test` – currently provided **only for evaluation**.  
- `train` – will be released soon to support model development and fine-tuning.  

### Example Data
Each entry in the dataset follows the structure below:

```json
{
  "index": 0,
  "audio_fname": "0.wav",
  "text": "8년 전 3월 이전 하순 통영에서 지점별 가장 1시간최다강수량이 많은 날 검색해주세요.",
  "sr": 48000,
  "duration": 6.49
}
```

* `index`: unique ID
* `audio_fname`: corresponding audio file name (WAV format)
* `text`: ground-truth transcription
* `sr`: sampling rate (Hz), typically 48kHz but not guaranteed
* `duration`: audio length in seconds

## Statistics

| Metric               | Value                       |
| -------------------- | --------------------------- |
| Num. samples         | 5,492                       |
| Utterance time (sec) | 7.05 ± 2.55                 |
| └ min / max time     | 0.92 / 29.98                |
| Avg. chars / words   | 24.49 ± 15.62 / 7.59 ± 4.34 |
| Unique words         | 4,955                       |
| Absent ratio (%)     | 24.86                       |

## Citation
```
@article{park2024evaluating,
  title={Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts},
  author={Park, ChaeHun and Cho, Hojun and Choo, Jaegul},
  journal={arXiv preprint arXiv:2410.18444},
  year={2024}
}
```