Multilingual Speech and Dialogue Data

Building a Multilingual Transcription Pipeline

Customer Story 01 · Multilingual Speech and Dialogue Data

The Challenge:

A frontier AI lab building a multilingual language model needed speech data in 25 languages, with no team, tools, or process to produce it. Linguists had to capture exactly what was said, plus emotion, background sounds, and even silences, across recordings with one or several speakers.

Effective Solution:

Volga ran the whole operation, from finding and onboarding native-speaking linguists to quality checks and delivery. Volga's own speech recognition tool drafted each transcript, saving at least 40% of manual effort. Automated checks caught structural errors, and human linguists made the finer language calls. Approved transcripts were converted into JSON ready for training, with a team working around the clock, weekends included.

Results:

Volga built a complete speech operation from scratch and became the client's largest vendor by volume.

Up and running in just 2 days

2,000+ linguists brought on in under 6 weeks

25 languages covered within 8 weeks

10,000+ hours of audio handled

1,200,000+ recordings delivered

95% accuracy

Emotion-Aware Dialogue Datasets

Customer Story 02 · Multilingual Speech and Dialogue Data

The Challenge:

A conversational AI developer wanted a chatbot that talks like a real person, with the right emotion, tone, and cultural feel, not just correct grammar. That meant fresh, native-level conversations in many languages, labelled well enough to train on.

Effective Solution:

Volga paired native-speaking contributors by age, profession, and communication style, gave them a scenario, and had them chat in real time. Every message was tagged for emotion, tone, and intent, then checked by automated tools and human reviewers before delivery as clean JSON.

Results:

A chatbot that once sounded scripted now sounds like a real person, whether it's a boardroom meeting or friends joking around.

50,000+ messages written from scratch by native speakers

10+ languages, each with its own slang, humor, and local feel

Every message labelled for emotion, tone, and intent, so the data shows how and why things were said, not just what

Real back-and-forth conversations, with hesitation, sarcasm, and empathy

Multilingual Speech and Dialogue Data

Building a Multilingual Transcription Pipeline

The Challenge:

Effective Solution:

Results:

Emotion-Aware Dialogue Datasets

The Challenge:

Effective Solution:

Results:

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