Deepgram updates multilingual speech-to-text model with improved code-switching accuracy
The retrained Nova-3 Multilingual model lowers word error rates when languages are mixed within conversations
Deepgram, the AI speech recognition company, has updated its Nova-3 Multilingual speech-to-text model to improve accuracy across supported languages, with the largest gains in code-switching scenarios where speakers mix languages within single utterances or conversations.
The company said the updated model lowers word error rate across both batch and streaming inference.
Deepgram said Nova-3 Multilingual was retrained and evaluated on diverse multilingual benchmarks, highlighting advances in curriculum design and data curation to improve the model's exposure to code-switching examples and reduce word drops when languages are mixed.
The model supports dynamic Keyterm Prompting at inference time to bias transcriptions toward domain-specific terms and names, which the company said is particularly useful for call centres, voice agents and industry analytics.
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The updated model is live now as the default production Nova-3 Multilingual model and requires no API (application programming interface) or configuration changes, the company added.
Deepgram said new accounts receive $200 in credits, equivalent to more than 750 hours of transcription or 200 hours of speech-to-text on Nova-3.
The Recap
- Deepgram updated Nova-3 Multilingual to reduce transcription errors.
- Credits equal over 750 hours or 200 hours across Nova-3.
- The updated model is live and requires no configuration changes.
