| --- |
| 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**. |
|
|
