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AssemblyAI boosts speech-to-text accuracy with keyterm prompting

By PulseAugur Editorial · [1 sources] ·

AssemblyAI has introduced "keyterm prompting" to improve the accuracy of its real-time speech-to-text models, particularly for specialized terms like names, jargon, and product names. This feature addresses the common issue where models correctly transcribe general speech but fail on high-value, rare words. Keyterm prompting works through two stages: real-time word-level boosting during inference and a post-turn phonetic matching process using Metaphone to correct misheard terms. This functionality is available on AssemblyAI's Universal-3 Pro and Universal-3.5 Pro Realtime models. AI

IMPACT Enhances the practical usability of real-time speech-to-text for specialized applications by improving accuracy on critical terms.

RANK_REASON The item describes a new feature for an existing speech-to-text service, not a core AI model release or research breakthrough.

Read on AssemblyAI blog →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AssemblyAI boosts speech-to-text accuracy with keyterm prompting

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Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
The item describes a new feature for an existing speech-to-text service, not a core AI model release or research breakthrough.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, infra
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High
Clearly on-topic for AI-industry coverage.
Story freshness
95 days old
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Full methodology in our editorial standards.

COVERAGE [1]

  1. AssemblyAI blog TIER_1 English(EN) ·

    Keyterm Prompting: Boost Names, Jargon & Product Terms

    Real-time models miss the words that matter most — names, SKUs, medications. Here's how keyterm prompting boosts them, how it works, and how to use it right.

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