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Service

Speech & Audio

Corpshore AI builds speech and audio datasets for TTS and ASR across 35+ languages, including rare and low-resource languages, Swahili and Uzbek TTS datasets that reduced word error rate from 11.2% to 4.3%.

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11.2% to 4.3%
word error rate on target languages
35+
languages, native speakers
97%+
accuracy via three-tier QA
50-70%
cost advantage vs US-domestic
What we deliver

Scoped, staffed, and QA-gated

  • TTS datasets with native, dialect-aware speakers
  • ASR training data and transcription
  • Code-switching and conversational speech
  • Studio-grade and in-the-wild audio capture
TTSASRTranscriptionDiarizationPronunciation
Speech & Audio at Corpshore AI
Pain points we solve

The problems this service addresses

Word error rate in non-English languages is high enough that voice features are not shippable in those markets.
Off-the-shelf datasets are English-first and miss regional pronunciation, so models trained on them fail on real speech.
Natural code-switching between local languages and English is absent from available data, so the model stumbles on mixed-language utterances.
Dialect variation is flattened into a single generic voice, which does not match how people in each region actually speak.
Low-resource languages have almost no usable public audio, so buying more third-party data does not close the gap.
Existing recordings lack accurate, dialect-aware transcripts, so there is nothing clean to train or evaluate on.
Case scenarios

Where teams use this work

Illustrative examples of how this service fits real programs. They are representative use cases, not named clients.

A voice assistant entering East Africa and Central Asia

A speech team needs Swahili and Uzbek TTS and ASR data covering regional pronunciation and code-switching, recorded by native speakers, because English-first datasets drive an unshippable word error rate.

A call-center analytics product

A team needs in-the-wild conversational audio with diarization and timestamps across accents and background noise, so its transcription and analytics hold up on real calls rather than clean studio speech.

A navigation app adding local voices

A team needs studio-grade TTS recordings across dialect variants, so generated voices sound native to each region instead of a single flattened accent.

A media company transcribing an audio archive

A team has existing recordings but needs native speakers to produce accurate, dialect-aware transcripts with diarization and pronunciation labels for training and search.

How we deliver

From scope to delivery, end to end

Step through the stages of a speech & audio engagement.

1. Define the language and dialect spec

Set the target languages, dialect variants, and code-switch patterns to cover, and whether capture is studio-grade, in-the-wild, or both.

Stage 1 of 5
Try it

A live look at the work

Switch tabs to see how a labeling, preference, or transcription unit moves through the QA cascade.

QA cascade active
carpedestriancyclist
What to expect

How the engagement runs

  • It starts with the target languages, dialects, and code-switch patterns to cover, plus whether capture is studio-grade, in-the-wild, or both.
  • Native speakers are recruited in-region, who live in the language rather than diaspora or second-language voices.
  • Recording follows a written spec with prompts designed to elicit regional pronunciation and natural code-switching.
  • Every recording is transcribed and aligned by native speakers under the three-tier QA cascade before delivery.
  • You provide the language and dialect targets, any script or domain requirements, and your training format; Corpshore provides speakers, studios, transcription, and QA.
  • Improvement is validated on the exact target languages, so word error rate is measured on the markets the model needs to serve.
Insights

What doing this well requires

  • The gap in non-English speech models is coverage of pronunciation and code-switching, which is exactly what English-first datasets omit.
  • Building a dataset from capture through transcription keeps it clean at the source, rather than assembling it from mismatched third-party pieces.
  • Native, in-region speakers are non-negotiable for dialect fidelity, because how a language is actually spoken rarely matches a studio approximation.
  • Low-resource languages are where owned collection matters most, because usable off-the-shelf data barely exists to buy.
  • Blending studio-grade and in-the-wild audio lets a model generalize from clean speech to the noise and overlap production systems encounter.
97%+
accuracy via QA cascade
35+
languages, native in-region
15,000+
seats across 18+ countries
50-70%
cost advantage vs US-domestic
Quality

Every unit passes a three-tier QA cascade

Tier 1

Annotator + peer review

Trained in-region annotators label to a versioned taxonomy. Every unit gets a structured peer check before it moves.

Catches ~80% of errors
Tier 2

Expert QA lead

Domain QA leads audit sampled and flagged work, resolve edge cases, and feed corrections back into annotator guidance.

Catches ~15% more
Tier 3

Programmatic + consensus

Automated consistency checks, gold-set benchmarking, and consensus scoring gate the batch before delivery.

Locks in 97%+ accuracy
FAQ

Speech & Audio, answered

35+ languages, including rare and low-resource ones, recorded by native speakers who capture regional dialect and natural code-switching. Because the speakers live in the language, the data reflects real pronunciation and mixed-language speech rather than a studio approximation of it.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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