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Universal-1 achieves industry-leading performance in multilingual speech-to-text, with 10% or greater accuracy improvement over the next-best system in English, Spanish, and German.
It reduces hallucination rate by 30% on speech data and 90% on ambient noise, compared to a widely used open-source model.
Universal-1 also exhibits the ability to code-switch, transcribing multiple languages within a single audio file.
Additionally, it improves word-level timestamp accuracy by 25.5% relative to a popular open-source model and enables 5x faster parallel inference.
These advancements are the result of leveraging state-of-the-art ASR research and a robust system design.