Universal-3 Pro at the Miami GP ft. CallRail

2026-05-08 · Source: AssemblyAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Universal-3 Pro has been launched for Coral voice assistant customers, demonstrating robust performance in real-world audio environments. The model excels at transcribing complex entities such as names, emails, phone numbers, and alphanumeric sequences accurately. For instance, it can correctly process phrases like "Lewis Hamilton at Ferrari, he's number 44, right? He's from Great Britain" or an email address like "R Seams, rseams@assemblyai.com." This capability ensures high-quality transcription even in challenging conditions, such as those with significant background noise like racing cars at the Miami Grand Prix.

Key takeaway

For Machine Learning Engineers developing voice assistant products, Universal-3 Pro's demonstrated robustness in noisy environments suggests it can significantly improve transcription accuracy for critical data like names and emails. You should evaluate its performance against your specific real-world audio challenges to ensure reliable data capture and enhance user experience.

Key insights

Universal-3 Pro offers robust transcription of complex entities in noisy real-world environments.

Principles

• Transcription models must handle alphanumeric data.
• Real-world noise impacts voice assistant accuracy.

In practice

• Test transcription in high-noise settings.
• Prioritize models robust to diverse speech inputs.

Topics

• Universal-3 Pro
• Voice Assistant Technology
• Real-world Transcription
• Alphanumeric Data Capture
• Noisy Environment Performance

Best for: Machine Learning Engineer, NLP Engineer, AI Engineer, Director of AI/ML

Related on AIssential

• We Tested our Speech AI a DJ Booth at the Miami GP — Universal 3 Pro accurately transcribes speech in noisy, real-world environments with technical content.
• Live demo of AssemblyAI's Universal-3.5 Pro Realtime Speech-to-Text model — Contextual prompting and noise suppression significantly enhance real-time speech-to-text accuracy across diverse languages and challenging audio environments.
• What It Took to Reach 97% Accuracy in AI Answering Machine Detection — Achieving high AI detection accuracy requires iterative training with real production data and human-reviewed edge cases.
• Low-Burden Data Augmentation for Dysarthric ASR via Zero-Shot Voice Cloning — Zero-shot voice cloning effectively augments dysarthric speech data, improving ASR performance and bypassing costly data collection bottlenecks.
• Investigating Human-Model Discrepancies in Speech Quality Assessment via Acoustic and Prosodic Perturbations — Current MOS prediction models reliably track acoustic degradation but are insensitive to prosodic errors and misaligned on speaker characteristics.
• ElevenLabs and Google dominate Artificial Analysis' updated speech-to-text benchmark — ElevenLabs' Scribe v2 and Google's Gemini 3 Pro lead the latest speech-to-text benchmarks.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AssemblyAI.