Question 1

Which languages do you cover for speech?

Accepted Answer

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.

Question 2

What quality improvement can we expect?

Accepted Answer

On one program, native-speaker Swahili and Uzbek TTS and ASR datasets reduced word error rate from 11.2% to 4.3% on the target languages, unlocking reliable voice features for those markets. The gain came from covering regional pronunciation and code-switching that English-first datasets miss.

Question 3

Do you handle dialects?

Accepted Answer

Yes, with dialect-deep coverage. For example, Uzbek is captured across Tashkent, Samarkand, and Fergana variants, and Arabic across six variants including MSA, Gulf, Egyptian, Levantine, Moroccan Darija, and Iraqi. Dialect coverage is defined per project so the data matches the markets you serve.

Question 4

What speech data types do you deliver?

Accepted Answer

Text-to-speech (TTS) datasets with native, dialect-aware speakers, automatic speech recognition (ASR) training data and transcription, conversational and code-switching speech, and speaker diarization and pronunciation data. Capture ranges from studio-grade to in-the-wild depending on the acoustic conditions your model needs to handle.

Question 5

Do you record studio-grade or in-the-wild audio?

Accepted Answer

Both. Studio-grade capture gives clean, controlled audio for TTS and baseline ASR, while in-the-wild capture provides the noise, distance, and overlap that production speech systems actually encounter. Many programs combine the two so the model generalizes from clean to real-world audio.

Question 6

Can you transcribe and label existing audio?

Accepted Answer

Yes. Native speakers transcribe and annotate your existing audio, including diarization, timestamps, and pronunciation labeling, under the three-tier QA cascade. This is useful when you already have recordings but need accurate, dialect-aware transcripts to train or evaluate on.

Question 7

How do you handle low-resource languages?

Accepted Answer

Low-resource languages are where owned, in-region collection matters most, because usable off-the-shelf data barely exists. Corpshore recruits native speakers in-region and builds the dataset from capture through transcription and QA, so a language with little public data still gets a purpose-built, dialect-aware training set.

Speech & Audio

Scoped, staffed, and QA-gated

Every unit passes a three-tier QA cascade

Annotator + peer review

Expert QA lead

Programmatic + consensus

Speech & Audio, answered

Ready to scope a pilot?