KazMix-3

Kazakh three-speaker overlapping-speech dataset for target-speaker ASR (TS-ASR), released with the 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); 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 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.

@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}
}