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Vonage call transcription: adding real-time speech-to-text to Vonage

TL;DR: Integrating our speech-to-text infrastructure with the Vonage Voice API replaces fragmented recording, transcription, and enrichment stacks with a single API. By routing Vonage WebSocket streams directly to our endpoint, contact centers achieve approximately 270ms real-time latency for live agent assistance, or use post-call batch processing for automated QA scoring. Streaming is the right choice for live superviso. Async is the right choice when speaker-attributed QA scoring and full call context matter more than latency.

Speech-To-Text

Key data extraction: accurately extracting names, account numbers, and intents from calls

TL;DR: Downstream contact center automation fails silently when the transcription layer misinterprets a name, transposes a digit, or attributes speech to the wrong speaker. Every QA scorecard, CRM entry, and coaching signal is ceiling-bounded by the accuracy of the layer beneath it. A wrong digit or phonetic name substitution propagates into every CRM field and compliance event that follows. Extraction precision is capped by transcription quality: Solaria-1 delivers on average 29% lower WER on conversational speech and 3x lower DER than alternatives, benchmarked across 8 providers, 7 datasets, and 74+ hours of audio.

Speech-To-Text

Amazon Connect transcription: real-time speech-to-text for AWS contact centers

TL;DR: Contact centers using Amazon Connect struggle with high transcription costs and poor multilingual accuracy when relying on native tools. Routing audio via Kinesis Video Streams or S3 to Solaria-1 eliminates the Lambda 15-minute timeout risk and removes per-feature add-on costs. On conversational speech, Solaria-1 delivers on average 29% lower WER than alternatives, benchmarked across 7 datasets and 74+ hours of audio.

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: