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Generative AI Technology

Multilingual Studio Voice Data Production for Advanced AI Voice Development

The Challenge

A global technology company need to produce a large-scale, multilingual voice dataset designed to support the development of more natural, expressive, and emotionally rich AI voice technologies. The project required professional studio recordings across five languages and locales: Hindi, Telugu, Canadian French, Arabic, and Korean.

For each language, the client required up to 36 hours of high-quality audio, combining scripted single-speaker content with prompted and unprompted dual-speaker conversations designed to capture natural speech and emotional expression.

The dataset needed to meet strict linguistic, acoustic, technical, and compliance requirements. This included recruiting professional male and female voice talent in each market, recording in qualified studios, capturing conversational speakers on separate audio channels, producing accurate verbatim transcripts, and maintaining consistent standards across multiple countries.

The voice performances also needed to deliver the appropriate energy, expressiveness, and conversational naturalness for each target language and locale.

• • • •The Solution• • • •

DataForce developed an end-to-end voice production program encompassing talent acquisition, acoustic qualification, recording, transcription, quality assurance, compliance, and delivery.

Professional male and female voice talent were recruited for each language and presented to the client for selection. Before full production began, DataForce qualified each recording environment and completed an initial calibration session to establish expectations for vocal style, pronunciation, pacing, conversational naturalness, technical output, energy, and emotional expressiveness.

Each language dataset included:

  • Up to 18 hours of dual-speaker conversational recordings
  • Up to 18 hours of scripted, single-speaker recordings
  • 48 kHz, 32-bit mono audio
  • Separate booths and audio channels for conversational sessions
  • Verbatim native-language transcripts reviewed by native teams

Voice talent worked to produce natural, engaging speech while capturing emotional reactions, changes in pace, hesitation, and realistic interactions between speakers. This combination of scripted and conversational data provided the client with both controlled linguistic coverage and the expressive variation needed to develop more natural AI voices.

All recordings underwent automated and manual audio quality checks. DataForce also used a hybrid transcription workflow that combined initial automatic speech recognition output with human correction and quality review by trained native speakers.

Recording, audio QA, and transcription activities were completed in parallel to accelerate delivery and identify potential issues before they affected additional sessions. Centralized program management ensured that local studios and language teams followed consistent technical, quality, and compliance standards.

Results

DataForce delivered a scalable, production-ready multilingual voice dataset covering all five languages and locales, totaling up to 180 finished hours of audio. The final dataset combined professionally recorded scripted and conversational speech, balanced male and female voice representation, and fully quality-checked audio.

Through centralized management and parallel production workflows, DataForce maintained consistent technical, linguistic, and compliance standards across all markets while achieving an average turnaround of approximately 30 calendar days per language. The client received clean, emotionally expressive, structured voice data that could be integrated directly into its AI voice development and evaluation workflows.