Panels — High Quality Audio Data

What

Panels is an audio data provider focused on Voice AI teams. It creates high-quality audio datasets for training and evaluation, with an emphasis on matching data to a customer’s specific model, use case, and quality requirements.

The product appears positioned for both frontier voice labs and early-stage startups that need production-ready speech data without building collection operations internally. Its workflow centers on researching dataset requirements, collecting and quality-controlling audio in-house, and then iterating based on model results and real-world performance gaps.

Features

• Speaker-separated multilingual audio datasets: Panels offers a proprietary large-scale dataset with speaker-separated audio across multiple topic domains, which can support training and evaluation for conversational voice systems.
• Single-speaker scripted recordings: It provides single-speaker audio captured across varied recording environments, useful for models that need controlled speech examples with environmental diversity.
• Turn-taking evaluation data: Panels includes multilingual datasets designed to evaluate human-agent turn taking in task-driven, real-world scenarios, which is valuable for voice assistant responsiveness testing.
• Custom dataset design: Teams can define their own data requirements, allowing Panels to tailor speakers, environments, edge cases, and acceptance criteria to a specific product need.
• End-to-end in-house production: Panels handles collection, QA, transcription, and review internally, which can reduce coordination overhead and improve consistency across the dataset pipeline.
• Iterative dataset expansion: After an initial dataset is in use, Panels helps expand coverage into accents, noise conditions, domains, and long-tail scenarios based on observed model gaps.

Helpful Tips

• Define evaluation goals early: For this type of vendor, the clearest outcomes come when teams specify whether the dataset is meant for training, benchmarking, turn-taking evaluation, or gap-filling in production performance.
• Request representative samples: Since Panels offers samples for off-the-shelf datasets and bespoke samples are typically available within two weeks, sample review should be used to validate recording quality and fit before scaling.
• Set acceptance criteria in operational terms: Requirements such as speaker separation, noise conditions, transcription quality, and edge-case coverage should be documented concretely to avoid ambiguity during collection.
• Plan for iteration, not one-time delivery: The site emphasizes ongoing refinement, so buyers should expect the strongest value when dataset updates are tied to model evaluation results and deployment feedback.
• Clarify exclusivity needs upfront: Panels states that exclusivity rights are available for audio data, so teams with defensibility or competitive concerns should address that during scoping.