---
license: cc-by-4.0
pretty_name: Mixed-Language Speech Detection Pilot
task_categories:
- audio-classification
language:
- tr
- kmr
- ckb
- ar
- fa
- en
size_categories:
- 1K<n<10K
configs:
- config_name: default
  drop_labels: true
  data_files:
  - split: train
    path: train/**
  - split: validation
    path: validation/**
  - split: test
    path: test/**
---

# Mixed-Language Speech Detection Pilot

This dataset is a 6,000-clip binary audio-classification pilot for detecting
whether an utterance contains one language (`label = 0`) or more than one
language (`label = 1`). It covers Turkish (`tur`), Northern Kurdish/Kurmanji
(`kmr`), Central Kurdish/Sorani (`ckb`), Arabic (`ara`), Persian (`fas`), and
English (`eng`).

## Dataset composition

| Construction | Mixed | Monolingual | Total |
|---|---:|---:|---:|
| Single-call OmniVoice | 500 | 500 | 1,000 |
| Segment-level OmniVoice | 1,500 | 1,500 | 3,000 |
| Real-speech concatenation | 1,000 | 1,000 | 2,000 |
| **Total** | **3,000** | **3,000** | **6,000** |

The split is 80/10/10: 4,800 train, 600 validation, and 600 test clips. Cloned
voice references, source recordings, and text pools are isolated by split.

## Audio

- 16 kHz mono FLAC
- 60% clean
- 20% synthetic colored background noise
- 10% telephone/radio filtering
- 10% compression or short-room reverberation

Augmentation is applied after complete clip assembly and has the same
distribution for both labels.

## Synthetic speech

Synthetic clips were generated with `k2-fsa/OmniVoice`. Twenty distinct
synthetic reference voices were first created with OmniVoice voice design, then
used through the voice-cloning interface. Twelve references are exclusive to
train, four to validation, and four to test. No real person's voice was cloned.

For segment-level mixed clips, 80% use one cloned voice across all languages and
20% alternate between two cloned voices. Switch patterns are approximately 70%
A→B, 25% A→B→A, and 5% A→B→A→B. Segment boundaries in `segments` reflect the
assembled audio before length-preserving full-clip augmentation.

Single-call mixed generation uses language-agnostic conditioning and should be
treated as the experimental subset described by `construction =
"single_call_tts"`. It is intended primarily for clip-level evaluation.

## Real speech

Real concatenations use recordings from:

- Google FLEURS (`google/fleurs`), CC BY 4.0
- KMR-Tatoeba (`aranemini/kmr-tatoeba`), CC BY 4.0
- LibriSpeech `dev-clean`, CC BY 4.0

Source recording IDs and corpora are retained in each real sample. No source
recording is used more than three times or shared across dataset splits. Half of
the real joins are same-corpus and half different-corpus, independently for
mixed and monolingual labels.

## Fields

- `audio`: viewer-decoded audio from `file_name`
- `id`: stable sample identifier
- `label`: `0` for monolingual, `1` for mixed-language
- `construction`: `single_call_tts`, `segment_tts`, or `real_concatenation`
- `languages`: unique ISO 639-3 language codes present
- `language_sequence`: ordered span languages
- `speaker_condition`: same or different voice condition
- `segments`: start/end time, language, text, and provenance per span; null for
  single-call generation
- `text`: complete input text for single-call generation; null otherwise
- `audio_condition` and `augmentation_detail`: full-clip audio treatment
- `reference_voice_ids`, `source_ids`, `source_corpora`: provenance
- `qc_*`: deterministic file-integrity and signal checks

## Validation and limitations

Every published file is checked for successful decoding, nonzero duration,
finite samples, sample rate, clipping, annotation consistency, exact class and
construction counts, split isolation, source reuse, and distribution targets.
ASR and audio-language-identification scores are intentionally not included.
Consequently, intended languages—especially in the experimental single-call
subset—are annotations of the generation request rather than guarantees from an
independent acoustic recognizer. Human listening is recommended before using
this pilot as a gold evaluation set.

## Licensing and attribution

The derived dataset is released under CC BY 4.0. Users should also cite the
upstream FLEURS, KMR-Tatoeba, LibriSpeech, and OmniVoice resources when using the
corresponding subsets. Text and source identifiers are included to support
auditing and attribution.