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Scaled Cognition launches voice model Voice

1 reports1 story updatesUpdated Oct 20 21:00

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On October 20, 2026, Scaled Cognition Inc. launched its text-to-speech model Voice. According to a SiliconANGLE report the same day, the model supports more than 25 languages and voice cloning, can be deployed via API calls or self-hosted, and focuses on accurate reading of details such as account numbers and addresses. The report said hosted API pricing is $9 per million characters, while self-hosting is $1 per million characters (excluding infrastructure); the company says that at a scale of 5 million characters, including infrastructure, the cost is about $13 per million characters, and it claims a speech generation latency of 70 milliseconds. Its Smart Pronunciation capability first determines the meaning of abbreviations and numbers before generating audio—for example, reading "Pay 1/2 by 1/2" as "pay half by January second"—and it can run without relying on APT or other large language models. There are currently no subsequent developments.

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Scaled Cognition Launches Customer Service Voice Model Voice

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  1. Scaled Cognition launches voice model Voice

    Source:SiliconANGLE

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Oct 20

  1. SiliconANGLE

    Scaled Cognition Launches Customer Service Voice Model Voice

    Scaled Cognition has released Voice, a text-to-speech model that supports more than 25 languages and voice cloning, and can be accessed via API or deployed self-hosted. It focuses on accurately reading aloud details such as account numbers and addresses. Managed API pricing is $9 per million characters, while self-hosting costs $1 per million characters (excluding infrastructure). The company says that at a scale of 5 million characters, including infrastructure, the cost is about $13 per million characters, and it claims a speech generation latency of 70 milliseconds. Voice's Smart Pronunciation capability first determines the meaning of abbreviations and numbers before generating audio—for example, reading "Pay 1/2 by 1/2" as "pay half by January second"—and it can run without relying on APT or other large language models.

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