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    Panels

    Panels

    Tech

    High-quality audio datasets for training and evaluating Voice AI models.

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    About Panels

    Panels: Powering the Next Generation of Voice AI

    Panels is a specialized data provider that helps voice teams acquire the high-quality audio datasets needed to build and ship better audio models faster. By working closely with frontier voice labs and early-stage startups, Panels curates production-ready data tailored to the specific training and evaluation needs of each team.

    Key Features

    • Speaker-Separated Audio: Provides proprietary, large-scale multilingual datasets featuring speaker-separated audio across diverse topic domains.
    • Single Speaker Scripted Data: Offers single-speaker audio recorded across a wide range of diverse environments.
    • Turn-Taking Evaluation: Delivers multilingual datasets specifically designed for evaluating human-agent turn-taking models in task-driven, real-world scenarios.
    • End-to-End In-House Collection: Manages the entire collection and production process internally, including rigorous QA, transcription, and review for reliability.
    • Iterative Dataset Expansion: Helps teams grow coverage over time by identifying gaps and expanding into new accents, noise conditions, and long-tail scenarios.
    • Customizable Data Solutions: Allows teams to design their own datasets with specific requirements, acceptance criteria, and optional exclusivity rights.

    Use Cases

    • Frontier voice labs and startups training the next generation of Voice AI models
    • Teams needing to evaluate human-agent turn-taking in real-world scenarios
    • Organizations requiring bespoke, exclusive audio datasets with specific accents or noise conditions

    Getting Started

    Panels equips Voice AI developers with the precise, high-quality audio data required to train robust models. Through a rigorous process of research, collection, and iteration, the platform ensures that voice teams can confidently scale their products and improve performance across diverse real-world environments.