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Mastering real-time transcription: speed, accuracy, and Gladia's AI advantage

TL;DR: Most use cases like meeting assistants, post-call analytics, and note-taking tools don't need real-time transcription. Async delivers higher accuracy and better speaker attribution because the model processes the complete recording. Sub-300ms latency is a functional requirement only for voice agents, live captions, and live agent assist tools where immediate output is non-negotiable. Gladia's Solaria-1 delivers around 270ms average latency with 100+ language support and native code-switching for the use cases that do require it.

Speech-To-Text

Automated call scoring: Best practices for AI-powered QA and performance

TL;DR: Most contact centers manually review only a fraction of calls, leaving coaching decisions based on incomplete data. Automated call scoring closes that gap by combining async transcription with LLM-based evaluation, but every downstream score is bounded by the accuracy of your STT layer. When it fails on accented speakers or multilingual audio, compliance scores, sentiment flags, and coaching alerts all break, making STT engine selection the highest-leverage infrastructure decision in your QA stack.

Speech-To-Text

Generate automated follow-up emails from meeting recordings with Gladia and Claude

TL;DR: The bottleneck in automated meeting follow-ups is not the LLM writing the email. It's the transcription layer feeding it: wrong speaker labels and missed entities produce emails that sound generic or silently corrupt your CRM. Building your own pipeline with Gladia and Claude gives you predictable per-hour billing and strict data controls on paid tiers, backed by Solaria-1's on average 29% lower WER than competing APIs on conversational speech.

Ebook: Ultimate guide to using LLMs with speech recognition

Published on Jan 7, 2025

Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.

If you’re looking to combine speech recognition (STT) and LLMs for cutting-edge voice apps, look no further! Our ultimate guide is finally here, and it’s filled with valuable strategies and hands-on insights from our work with hundreds of audio-first companies and extensive interviews with experts in AI note-taking, sales enablement and customer support.

What you'll learn: