API Comparison Table

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From call audio to CSAT: Mapping contact center sentiment to CX signals

TL;DR: Manual QA teams sample 2–5% of contact center calls, leaving more than 95% of customer interactions unscored. Transcript errors propagate directly into your sentiment layer: a single substitution that flips "can't" to "can" inverts the sentiment signal before your classifier runs, making transcription quality a direct input to CSAT reliability. To automate quality assurance at 100% coverage, solve the transcription layer first. This playbook maps the audio-to-CSAT pipeline, explains where transcript errors compound into false QA scores, and shows the four production steps required to scale sentiment analysis across noisy, multilingual Business Process Outsourcing (BPO) environments.

Speech-To-Text

Integrating speech-to-text into your EHR: epic, athenahealth and FHIR

TL;DR: The real engineering work in EHR speech integration is mapping unstructured audio payloads to the correct FHIR resources, managing SMART on FHIR OAuth 2.0, and building resilient async write pipelines that survive rate limits and EHR downtime. On Growth and Enterprise plans, customer data is never used for model training, which is an important baseline control for any clinical pipeline handling PHI. The architectural patterns in this guide apply whether you choose a managed STT API or build the transcription layer yourself.

Speech-To-Text

European-language speech-to-text: evaluating coverage and accuracy

TL;DR: Academic benchmarks fail to predict production STT performance in European business environments, where accented speech, code-switching, and telephony noise push real-world Word Error Rate well above what clean read-speech datasets suggest. Engineering and ML Leads evaluating STT infrastructure need three metrics standard benchmarks don't capture: real-world WER on accented audio, Language Adherence Violation Rate for code-switching performance, and total cost of ownership (TCO) including engineering toil for self-hosted GPU clusters. On Switchboard, the most demanding conversational telephone dataset, Solaria-3 ranks #1 ahead of AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics: a concrete example of how production-relevant benchmarking changes the vendor picture. This guide provides the technical framework to run a statistically valid evaluation against your own audio distribution before committing to any vendor or build decision.

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: