ASR datasets · Speaker Diarisation
ASR Training Data for Speaker Diarisation
Transcribed speech corpora built to train and evaluate automatic speech recognition, with verbatim transcription, timestamps and per-token language tagging where code-mixing occurs. Applied to speaker diarisation, the specification is driven by one thing: diarisation error rate.
- Service
- ASR datasets
- Use case
- Speaker Diarisation
- Primary metric
- Diarisation error rate
Required data profile
- Per-speaker isolated channels with a mixed reference
- Genuine overlap preserved
- Turn-level ground truth
Technical specification
| Parameter | Standard |
|---|---|
| Audio | 16 kHz or 48 kHz PCM WAV |
| Transcription | Verbatim, including disfluencies, false starts and fillers |
| Timestamps | Utterance level by default; word level on request |
| Tagging | Noise, overlap, unintelligible, foreign-language and code-switch tags |
| Normalisation | Raw and normalised text columns delivered separately |
| Split | Train/dev/test splits with no speaker leakage across splits |
Process
- Style guide authored per language, covering numerals, loanwords, script and disfluency rules
- Transcriber calibration round with inter-annotator agreement measurement
- First-pass transcription
- Second-pass native review
- Automated consistency checks against the style guide
- Split generation with speaker-disjoint partitions
Metrics this feeds
- Diarisation error rate
- Overlap detection recall
- Speaker-count accuracy
Failure modes to design out
- Overlap edited out during recording
- Single-channel-only capture leaving no reliable ground truth
Agreement is measured, not assumed. We report word-level agreement on a held-out sample so you can judge label quality before training on it.
Deliverables
- Audio plus aligned transcripts (JSON/TSV, or your schema)
- Style guide as delivered documentation
- Inter-annotator agreement report
- Speaker-disjoint train/dev/test splits
Frequently asked
Is asr datasets the right service for speaker diarisation?
It covers per-speaker isolated channels with a mixed reference. Most speaker diarisation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.
What languages are available?
All 14 languages in the network plus Indian English accent bands.
How is the evaluation set handled?
Collected first, from speakers disjoint from the training cohort, so improvement is measurable.
Scope asr datasets for speaker diarisation
Send the metric you need to move.