Universal-3.6 Pro Realtime: trained on real conversations, built for real voice agents

Universal-3.6 Pro Realtime is a streaming speech-to-text model designed for voice agents and call centers, where brief responses, noisy telephone audio, accents, background voices, and spoken entities such as phone numbers can affect downstream actions. Trained on tens of thousands of hours of real voice-agent conversations, it reportedly reduces short-response errors by 45%, language-confusion sessions by 74%, background-speech leakage by 29%, and normalized word error rates by 12% versus Universal-3.5 Pro on the company’s 12,460-scenario English benchmark, with larger gains in heavy noise. The release improves turn detection through training on incomplete speech and introduces entity-aware endpointing, which holds turns open for numbers, IDs, addresses, and similar information, reducing phone-number entity errors while increasing latency only in the slowest cases. An optional voice-focus feature uses filtered audio to suppress a nearby competing speaker, producing substantial gains in tests containing second talkers but modest accuracy costs when no competing speaker is present. The model also adds 14 languages, including Korean and Russian, expands multilingual training, and reports reduced code-switching errors; in the company’s competitive tests it ranks first among 21 systems for English voice-agent word and entity accuracy and code-switched language pairs, while placing third on average across broader multilingual read-speech benchmarks.

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