What is Word Error Rate (WER): How it’s calculated, and why it can mislead

Word Error Rate (WER) is a metric that evaluates the performance of ASR systems by analyzing the accuracy of speech-to-text results. WER metric allows developers, scientists, and researchers to assess ASR performance. A lower WER indicates better ASR performance, and vice versa. The assessment allows for optimizing the ASR technologies over time and helps to compare speech-to-text models and providers for commercial use.

Build a customer interview library with Gladia, Airtable & Make.com

TL;DR: Most product teams lose qualitative insights to scattered audio and transcripts that misattribute quotes. A reliable interview library needs accurate async diarization, automated routing, and a searchable database. Gladia's Solaria-1 sets the accuracy floor (29% lower WER, 3x lower DER on conversational speech), and Make.com routes its structured JSON into Airtable automatically, turning raw recordings into a searchable, theme-tagged customer content library.

Build an automated sales call analyzer with Gladia and n8n

TL;DR: Off-the-shelf conversation intelligence platforms cost $1,200 to $2,400 per seat per year, while this n8n and Gladia pipeline scales at $0.20 to $0.61 per hour of audio with all features included. The async pipeline handles transcription, speaker diarization, and audio intelligence in a single API call, and the structured JSON output maps directly into HubSpot or Salesforce through n8n nodes. Gladia's Solaria-1 model covers 100+ languages, including 42 that no other API-level competitor supports, protecting CRM data quality for global sales teams.

How to build a no-touch pipeline from sales calls to CRM

TL;DR: Manual CRM entry breaks sales intelligence pipelines because reps skip fields and misremember details, creating corrupted deal data that spreads into forecasts, coaching scores, and follow-up tasks. The bottleneck in fixing this isn't the CRM API or the LLM prompt, it's the transcription layer, since a high word error rate corrupts every entity Claude extracts downstream. This tutorial walks through a production-ready pipeline using Gladia's async STT for transcription, Claude for entity extraction, and n8n for orchestration, with most teams reaching production in under 24 hours. Gladia's Solaria-1 model delivers on average 29% lower WER than alternatives on conversational speech, directly protecting the accuracy of every deal record written to the CRM.

Build a lead scoring pipeline from sales call recordings with Gladia and Claude

TL;DR: Accurate hot/warm/cold lead scoring needs speaker-attributed transcripts. Without diarization, Claude cannot separate prospect buying signals from the sales rep's talk track, so any score is unreliable. Gladia's async API (Solaria-1) returns speaker-labeled, LLM-ready JSON, with diarization, sentiment, and named entity recognition included in the base per-hour rate on Starter and Growth plans, each enabled explicitly in the request. On Growth and Enterprise plans, audio is never used for model training with no opt-out required, keeping the pipeline safe for sensitive sales calls under GDPR and SOC 2 Type II.

How to flag low-confidence spans in AI meeting transcripts for reviewer QA

TL;DR: Transcription errors silently corrupt meeting summaries and CRM entries. Flag uncertainty with word-level confidence scores and pattern matching, then sync only the flagged spans to audio timestamps so reviewers verify the low-confidence parts instead of the whole transcript. Gladia's async API provides word-level confidence, pyannoteAI Precision-2 diarization, and native code-switching detection out of the box.

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.