README.md

---
dataset_info:
  features:
  - name: interview_id
    dtype: int64
  - name: segment_id
    dtype: string
  - name: role
    dtype: string
  - name: start
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  - name: end
    dtype: float64
  - name: transcript
    dtype: string
  - name: word_tokens
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    - name: start
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    - name: start
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    - name: word
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  - name: audio
    dtype:
      audio:
        decode: false
  - name: student_sex
    dtype: string
  - name: state
    dtype: string
  - name: town_city
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  - name: recording_year
    dtype: string
  - name: institution
    dtype: string
  splits:
  - name: train
    num_bytes: 29777459994
    num_examples: 107344
  download_size: 20477129079
  dataset_size: 29777459994
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-nc-4.0
language:
- en
---

# MD_NLP

## Dataset Description

**MD_NLP** is a discourse-annotated, word-aligned, and georeferenced corpus derived from the narrative portion of the Mitchell–Delbridge recordings, a large mid-20th-century archive of Australian English. The corpus was constructed from archival WAV recordings using an automated pipeline combining WhisperX-based ASR, neural speaker diarization, LLM-assisted discourse-role correction, and Montreal Forced Aligner boundary refinement.

The released dataset consists of short, role-consistent narrative segments with transcripts, word-level timestamps, linked audio, and selected sociodemographic metadata.

- **Curated by:** Steven Coats
- **Institution:** University of Oulu
- **Language(s):** English (Australian English)
- **License:** cc-by-nc-4.0
- **Related paper:** *MD_NLP: Reconstructing an Australian English Heritage Dialect Corpus from the Mitchell–Delbridge Recordings through LLM-Assisted Speaker Attribution*

## Dataset Summary

The source archive comprises recordings of 7,735 Australian secondary school pupils from 327 locations across Australia, recorded in 1959–1960. MD_NLP includes the spontaneous narrative component of these recordings rather than the read word-list and sentence materials more commonly used in previous research.

The dataset is intended for research on:

- Australian English variation
- dialectology and sociolinguistics
- discourse structure and turn-taking
- corpus phonetics
- ASR, diarization, and alignment on legacy speech recordings

## Dataset Structure

Each row corresponds to a short, role-consistent segment.

### Fields

- **interview_id**: numeric interview identifier
- **segment_id**: unique segment identifier
- **role**: discourse role label (`Student` or `Teacher`)
- **start**: segment start time in seconds
- **end**: segment end time in seconds
- **transcript**: transcript text for the segment
- **word_tokens**: list of word-level tokens with start and end times
- **audio**: path/reference to the corresponding audio segment
- **student_sex**: recorded sex metadata for the student
- **state**: Australian state or territory
- **town_city**: locality
- **recording_year**: recording year
- **institution**: school/institution name

### Split

The current release contains one split:

- **train**: 257,357 segments

## Corpus Size

- **Recording length:** 214.14 hours
- **Speech duration:** 137.95 hours
- **Turns:** 71,929
- **Word count:** 1,791,856

Role-based totals:

| Metric | Student | Teacher | Total |
|---|---:|---:|---:|
| Speech duration (h) | 92.71 | 45.24 | 137.95 |
| Turns | 46,026 | 25,903 | 71,929 |
| Word count | 1,155,994 | 635,862 | 1,791,856 |

## Source Data

Mitchell, Alexander George and Arthur Delbridge. (1998). The speech of Australian adolescents:
Research data and recordings collected by AG
Mitchell and Arthur Delbridge in 1959 and 1960.
The University of Sydney. https://doi.org/10.25910/jkwy-wk76 

The dataset is derived from the Mitchell–Delbridge recordings, made by schoolteacher volunteers in 1959 and 1960 in 327 locations across all Australian states and territories. The original archive contains read materials and a short narrative component. MD_NLP includes only the narrative recordings.

The narratives typically involve brief teacher–student interaction, though some recordings are more monologic. Recording conditions vary substantially across sites.

## Processing

The corpus was created using the following pipeline:

1. **WhisperX** for automatic speech recognition and initial word alignment
2. **Pyannote** for speaker diarization
3. **LLM-assisted discourse-role correction** (Gemini 2.5-flash) to distinguish `Teacher` and `Student`
4. **Montreal Forced Aligner (MFA)** for boundary refinement
5. Reconstruction into short, role-consistent segments with word-level timing

The released transcripts preserve the original WhisperX tokenization while using refined timestamps where alignment succeeded.

## Evaluation

Speaker-role attribution was evaluated on 10 manually checked narratives (approximately 30 minutes of speech; 185 turns).

| System | Accuracy |
|---|---:|
| Baseline (WhisperX + Pyannote) | 62.70% |
| Full pipeline (LLM-assisted) | 95.68% |

These results indicate that the LLM-assisted role-correction step substantially improves turn-level speaker attribution in interview-style archival recordings.

## Intended Use

MD_NLP is intended for research use, especially for:

- regional and social variation in Australian English
- discourse and interactional structure
- corpus phonetics and time-aligned speech analysis
- geographically explicit dialect research
- evaluation of ASR, diarization, and alignment methods on legacy speech

## Limitations

- The corpus is derived from archival recordings with variable audio quality.
- Some interviewer speech is faint, partially absent, or missing.
- Transcripts are automatically generated and corrected, not manually transcribed throughout.
- Some alignment boundaries may remain imperfect despite MFA refinement.
- Metadata reflect archival source records and may contain inconsistencies or omissions.

## Sensitive Information

The dataset contains speech-derived transcripts and linked metadata fields such as sex, institution, state, town/city, and recording year. These are historical archival data. Users should handle the dataset in accordance with the license and any archive-specific restrictions.

## Citation

If you use this dataset, please cite the associated paper.

**BibTeX**
```bibtex
@inproceedings{coats2026mdnlp,
  title={MD\_NLP: Reconstructing an Australian English Heritage Dialect Corpus from the Mitchell--Delbridge Recordings through LLM-Assisted Speaker Attribution},
  author={Coats, Steven},
  booktitle={Proceedings of LREC 2026},
  year={2026}
}