---
license: cc-by-4.0
language:
- kk
- ru
task_categories:
- automatic-speech-recognition
tags:
- kazakh
- russian
- code-switching
- speech
- asr
- benchmark
pretty_name: Kazakh Code-Switching ASR Benchmark
size_categories:
- n<1K
---

# Kazakh Code-Switching ASR Benchmark

A benchmark for evaluating ASR systems on **natural Kazakh speech that
code-switches with Russian** — the everyday Kazakh–Russian mixing found in
stand-up, interviews and vlogs, not scripted read speech. This is, to our
knowledge, the first speech/ASR resource targeting the Kazakh–Russian
code-switching pair (existing Kazakh–Russian NLP resources are text-only).

Code, scoring harness and full analysis:
**https://github.com/Tim2190/Kaz-ASR-codeswitch-benchmark**

## Dataset structure

31 clips (~3.7 min total, clips ≤ 20 s), sourced from CC BY YouTube material.
Each clip carries **two independent reference transcripts** and four binary
phenomenon flags:

| Field | Description |
|---|---|
| `file_name` | path to the WAV clip |
| `audio_id` | clip identifier |
| `transcript_verbatim` | literal transcript, exactly as spoken (reductions kept) |
| `transcript_normalized_written` | dictionary word forms |
| `has_contraction` | fast-speech reductions present |
| `has_dialect_slang` | dialectal/slang lexis present |
| `has_barbarisms` | a Russian insertion where a native Kazakh word exists |
| `has_propers` | proper nouns present |
| `audio_source_link` | source channel/title (CC BY attribution) |

Audio is 48 kHz stereo 16-bit WAV. The two-layer design separates genuine
recognition errors from a system's tendency to auto-normalize morphology.
Full annotation rules are in `annotation_methodology.md` in the GitHub repo.

## Loading

```python
from datasets import load_dataset
ds = load_dataset("Tim2190/kazakh-codeswitch-asr", split="train")
print(ds[0]["audio"], ds[0]["transcript_verbatim"])
```

## Benchmark results (5 systems, WER / CER, lower is better)

| System | Type | WER | CER |
|---|---|---:|---:|
| Fine-tuned Kazakh Whisper | fine-tuned | **11.9%** | **3.8%** |
| Yandex SpeechKit | commercial STT | 17.1% | 6.5% |
| Gemini 2.5 Flash | commercial LLM | 29.5% | 12.5% |
| Whisper large-v3 | base, zero-shot | 42.5% | 13.0% |
| Google Cloud STT | commercial STT | 64.9% | 38.1% |

Key finding: commercial services span the entire quality range (Yandex 2nd,
Google last), and Russian insertions are the dominant error driver — errors
concentrate on the switched-in Russian words. See the GitHub repo for the
per-phenomenon breakdown and word-level analysis.

## License & attribution

Source audio is derived from Creative Commons **CC BY** YouTube material; the
originating channel/title for every clip is in the `audio_source_link` field.
Annotations and code are MIT-licensed. Created by **Timur Seidalin**.