Middle East & Europe · Latin
Turkish Noisy Speech Dataset
Turkish noisy speech sourced to your specification — no inventory, no fixed listing. This page covers what is specifically hard about this pairing, and which fields your specification needs to pin down.
Why noisy speech in Turkish is its own problem
Turkish long words are the first thing noise truncates — agglutinative suffixes sit at the end of the word, so masking the tail means losing tense and person information outright. That puts Turkish noise-robust data under a stricter SNR requirement than languages with short word forms.
The field to pin down first: Annotate word-final clarity or provide a per-word intelligibility score. A sentence-level SNR figure on its own is not enough.
At a glance
| Language | Turkish |
|---|---|
| Primary region | Middle East & Europe |
| Writing system | Latin |
| Category | Noisy Speech |
| Specification field to settle first | Annotate word-final clarity or provide a per-word intelligibility score. A sentence-level SNR figure on its own is not enough. |
| Delivery | Sourced to order, pilot batch before the full run |
What noisy speech data is
Speech collected under background noise — street, in-car, restaurant, office and other real environments — used for noise-robust models and speech enhancement.
What buyers get wrong about it
Noise type has to be reproducible. Buyers do not want vaguely noisy; they want a specified condition like restaurant chatter at 5 dB SNR.
The specification field that decides the quote
Signal-to-noise ratio (SNR) level and noise type must be annotated per file, or the data cannot be reproduced.
The language side: what Turkish demands
Turkish is an agglutinative language: one root can carry a long chain of suffixes, so the word list explodes. Istanbul and eastern accents also differ noticeably — if speaker origin is not recorded clearly, model generalization suffers.
What we can put in this delivery
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Read and conversational speech
Scripted recording for TTS, and unscripted conversation for recognition. Specified separately because they need different speaker pools.
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Transcription to your convention
Orthographic or phonetic, with the guideline written before production and shared with you for review.
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Speaker metadata
Age band, gender, region and dialect background per file, so you can slice the dataset rather than take it whole.
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Consent documentation
Signed speaker consent covering the intended use, plus collection methodology and the annotation guideline.
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Pilot batch
A small batch first, which you can reject. Misalignments surface after a few hours rather than at delivery.
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Delivery in your format
Audio format, sampling rate, segmentation length and metadata schema set to your pipeline's requirements.
What arrives in a delivery
| Component | What it is |
|---|---|
| Audio files | Format, sampling rate and segmentation length set to your pipeline. Named to a convention you specify. |
| Transcription | Orthographic or phonetic, produced under a guideline you review before production starts. |
| Speaker metadata | Age band, gender, region and dialect background per file, plus a speaker identifier so the dataset can be sliced. |
| Recording conditions | Environment, device and, where relevant, measured signal-to-noise ratio per file. |
| Annotation guideline | The document the annotators worked from, so you can reproduce the conventions on your own data. |
| Consent records | Signed speaker consent covering your intended use, with the transfer mechanism addressed where required. |
| Collection methodology | How speakers were recruited, screened and scheduled — the part that tells you how biased the pool is. |
| Quality report | Pilot outcome, re-work log, and the annotator agreement figures where the task supports measuring them. |
Questions we get about Turkish Noisy Speech
How many distinct speakers can you provide for Turkish Noisy Speech?
It depends on the specification and the timeline, and we will give a real number rather than a target. For this pairing, speaker recruitment is usually the step that sets the schedule. Annotate word-final clarity or provide a per-word intelligibility score. A sentence-level SNR figure on its own is not enough.
Can you annotate to our own guideline instead of the default?
Yes, and for this pairing we would recommend it. Turkish long words are the first thing noise truncates — agglutinative suffixes sit at the end of the word, so masking the tail means losing tense and person information outright. That is exactly the kind of decision a default guideline leaves open, and it is where annotators diverge. Send us your guideline, or we will draft one and you can review it before production starts.
Do you offer a sample before we commit to a full run?
Yes. Select a free sample in the request form and describe what you need. A pilot batch is the cheapest way to establish whether the quality bar is reachable for Turkish Noisy Speech before committing to the full volume.
Is the data licensed or owned outright?
Licensing terms are set per project, so tell us how the model will be used and whether it will be distributed. Consent documentation travels with the data either way, and we do not handle medical or clinical data or recorded telephone calls.
Other datasets in Turkish
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Turkish Code-Switching Speech
Speech that mixes two or more languages inside a single utterance — Hinglish, Spanglish, Taglish — used for recognition in real spoken settings.
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Turkish Children Speech
Speech data from child speakers, grouped by age band, used for children's speech recognition and children's education products.
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Turkish Audiobook
Continuous long-form narration in audiobook style, steady in intonation and generous in duration, the main source material for high-quality TTS.
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Turkish Short Video Speech
Speech in the talking-head style of short video — fast, emotionally strong, colloquial — used for short video subtitles and content understanding.
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Turkish Live Stream Speech
Long-session spoken content from live streams — host monologue and responses to audience interaction — fast-paced and heavily improvised.
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Turkish Voice Assistant
Command and dialogue data for voice assistants, usually with intent annotation, covering the full chain from wake word to understanding to response.
Noisy Speech in other languages
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Malayalam Noisy Speech
South Asia
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Nepali Noisy Speech
South Asia
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Sinhala Noisy Speech
South Asia
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Bangla Noisy Speech
South Asia
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Khmer Noisy Speech
Southeast Asia
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Arabic (Egyptian) Noisy Speech
Middle East & North Africa
Request Turkish Noisy Speech
Tell us the language, the hours, and what the data needs to look like. You will get a real number and a real timeline — not a range. If we cannot source it well, we will tell you that instead.
- Pilot batch before the full run, so problems surface early.
- Consent documentation delivered with the data.
- No medical or clinical data. No recorded telephone calls.