Central Asia & Caucasus · Latin
Uzbek Noisy Speech Dataset
Uzbek Noisy Speech delivered to your specification. A catalog entry, not a stock listing. This page covers what is specifically hard about this pairing, and which fields your specification needs to pin down.
Why Noisy Speech in Uzbek is its own problem
The standard Uzbek noisy scenes are bazaars, street markets, and shared taxis: sustained mid-frequency crowd noise rather than engine rumble. One detail specific to Uzbek recordings: many speakers count and quote measurements in Russian even inside an otherwise Uzbek sentence, and under noise that switch is hard to hear reliably. Annotators end up guessing whether a number was Uzbek or Russian, and the guess propagates into the transcript.
The field to pin down first: Annotate the noise scene item by item, and state per file whether numerals and measurements were spoken in Uzbek or Russian: after collection that is not recoverable from the audio.
At a glance
| Language | Uzbek |
|---|---|
| Primary region | Central Asia & Caucasus |
| Writing system | Latin |
| Category | Noisy Speech |
| Specification field to settle first | Annotate the noise scene item by item, and state per file whether numerals and measurements were spoken in Uzbek or Russian: after collection that is not recoverable from the audio. |
| 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 Uzbek demands
Uzbek is officially written in Latin script, but a large share of existing corpora is in Cyrillic. The conversion rules between the two leave a few letters without a clean one-to-one mapping, so conversion introduces errors.
What we can put in this delivery
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Collection to your specification
Scripted or unscripted, in the language, domain and conditions your task needs. Specified separately where the variants need different contributor pools.
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Labels to your convention
Orthographic, phonetic or categorical, with the guideline written before production and shared with you for review.
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Contributor metadata
The attributes that let you slice the set rather than take it whole. The ones your task turns on, not a fixed list.
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Consent documentation
Signed contributor 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
File format, structure, segmentation and metadata schema set to your pipeline's requirements.
What arrives in a delivery
| Component | What it is |
|---|---|
| The data files | Format, structure and segmentation set to your pipeline. Named to a convention you specify. |
| Labels or transcription | Orthographic, phonetic or categorical, produced under a guideline you review before production starts. |
| Contributor metadata | The attributes that let you slice the set: age band, gender, region, dialect background, or whatever your task turns on. |
| Collection conditions | Environment, device and, where it can be measured, signal quality: recorded per file. |
| Annotation guideline | The document the annotators worked from, so you can reproduce the conventions on your own data. |
| Consent records | Signed contributor consent covering your intended use, plus cross-border transfer terms where they apply. |
| Collection methodology | How contributors 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 Uzbek Noisy Speech Dataset
How many distinct speakers can you provide for Uzbek Noisy Speech Dataset?
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 the noise scene item by item, and state per file whether numerals and measurements were spoken in Uzbek or Russian: after collection that is not recoverable from the audio.
Can you annotate to our own guideline instead of the default?
Yes, and for this pairing we would recommend it. The standard Uzbek noisy scenes are bazaars, street markets, and shared taxis: sustained mid-frequency crowd noise rather than engine rumble. 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 Uzbek Noisy Speech Dataset 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 Uzbek
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Uzbek Speech Translation
Parallel data pairing source-language audio with target-language translation, used for speech-to-text and speech-to-speech translation models.
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Uzbek In-the-Wild Speech
Speech collected under fully natural conditions (no studio, no topic constraints), as close to real usage as collection gets.
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Uzbek Call Center Speech
Customer-service calls, simulated rather than intercepted, capturing both the agent and the caller side, used for call center QC, intent recognition, and dialogue systems.
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Uzbek Conversational Speech
Speech from natural conversation between two or more people, on open or semi-structured topics, used for conversational AI, voice assistants, and small talk models.
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Uzbek Singing Voice
Vocal recordings with melody, in both a cappella and accompanied form, used for singing voice synthesis, music information retrieval, and lyric alignment.
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Uzbek Speech Commands
Targeted recordings of short command words or phrases, usually with many speakers reading each entry several times over, used for wake words and on-device command recognition.
Noisy Speech in other languages
Request Uzbek Noisy Speech Dataset
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.
- You send the spec Language, hours, recording conditions, and the deadline.
- We reply within two business days A real number and a real timeline, not a range.
- You approve a pilot batch A small run first, so problems surface before the full order.
- Full run, then delivery with documents Audio, transcripts, and the consent record for every speaker.