Datasets:

ddehun
/

korean-weather-asr

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:

{
  "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}
}