---
license: cc-by-4.0
language: [kk]
task_categories: [automatic-speech-recognition]
tags: [target-speaker-asr, speech-separation, kazakh, overlapping-speech, enrollment]
pretty_name: KazMix-3
size_categories: [10K<n<100K]
---

# KazMix-3

Kazakh three-speaker overlapping-speech dataset for **target-speaker ASR (TS-ASR)**, released with the [Persona-ASR](https://github.com/IS2AI/Persona_ASR) project. Given a short **enrollment** utterance of a target speaker and a **3-speaker mixture**, the task is to transcribe only the target speaker, or reject the utterance when the target is absent.

This repository ships the **mixture manifests** and **generation scripts**, not the audio. Mixtures are derived from the **Kazakh Speech Dataset (KSD, [OpenSLR 140](https://www.openslr.org/140/))**; download KSD and run the scripts to reproduce the audio locally.

## Splits

| Split | Total | Positive (target present) | Negative (target absent) |
|---|---|---|---|
| `train_clean_100` | 62,775 | 41,850 | 20,925 |
| `val` | 13,500 | 9,000 | 4,500 |
| `test` | 13,500 | 9,000 | 4,500 |

Positive trials contain the enrolled target speaker and carry its transcript; negative trials do not contain the target (the model should emit no target transcript). Speakers are disjoint across splits.

## Files
- `train_clean_100_kazakh3mix_lufsfix_posneg.json`, `val_kazakh3mix_lufsfix_posneg.json`, `test_clean_kazakh3mix_lufsfix_posneg.json`
- `scripts/` — LUFS-uniform 3-speaker mixture generation (`rerender_kazakh3mix_lufs_uniform.py`) and manifest building.

## Manifest fields
| Field | Description |
|---|---|
| `mixture_audio` | 3-speaker mixture (16 kHz) |
| `enrollment_audio` | Target-speaker enrollment utterance |
| `transcript` | Target reference transcript (empty for negatives) |
| `speaker_id`, `target_speaker_id`, `target_index` | Target identity and its source index in the mixture |
| `sample_type`, `class`, `label`, `is_target_present` | Positive/negative trial labels |
| `snr_db`, `lufs_per_source`, `clip_scale` | Mixing SNR and per-source loudness (LUFS-uniform) |
| `s1_path`, `s2_path`, `s3_path`, `source_speaker_ids`, `clean_source_rel` | Source provenance for regeneration |

The full field list is present in each JSON entry.

## Mixing protocol
Three-speaker mixtures in the LibriMix `clean`/`max` style, loudness-normalized to a uniform LUFS across sources. Each mixture designates one target speaker; the enrollment is a *different* utterance from that speaker.

## Regenerating the audio
1. Download KSD from https://www.openslr.org/140/.
2. Run `scripts/rerender_kazakh3mix_lufs_uniform.py`, pointing it at your local KSD path (see the [Persona-ASR repo](https://github.com/IS2AI/Persona_ASR) for full setup and dependencies).

## License and citation
Manifests and scripts are released under CC BY 4.0; the underlying audio (KSD, OpenSLR 140) retains its own license. Please cite Persona-ASR and KSD.

```bibtex
@article{persona_asr,
  title   = {Persona-ASR: Bilingual Target-Speaker Speech Recognition for Kazakh--English Overlapping Speech},
  author  = {Meiramov, Rakhat and Rakhimzhanova, Tomiris and Taibassarov, Adil and Makhataeva, Zhanat and Varol, Huseyin Atakan},
  year    = {2026}
}
```