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Call center voice analytics: use cases, benefits, and how it works
TL;DR: Contact centers that rely on manual QA for call review typically sample only a small fraction of their total call volume, leaving the vast majority of audio unanalyzed. Voice analytics fixes this by converting raw phone calls into structured, LLM-ready data that feeds QA scorecards, CRM entries, and coaching workflows automatically. The catch is that telephony audio is uniquely hostile to standard speech APIs because narrowband codecs and packet loss break models trained on clean audio. This article explains the technical pipeline, the metrics that matter, and the infrastructure requirements that separate production-ready systems from vendor demos.
Customer sentiment analysis: methods, tools, and what voice data adds
TL;DR: Reliable sentiment analysis requires WER below 5%, speaker diarization that separates customer and agent emotion, and language models that hold performance across accents and code-switching. Text-only sentiment tools miss critical voice signals (pace, talk-over, vocal intensity) that predict churn before survey data surfaces the same risk. Automated sentiment scoring on high-accuracy transcripts shifts QA from sampling 2–5% of calls to monitoring 100% of them, the only coverage level at which churn risk and agent burnout surface early enough to act on.
Named Entity Recognition from call transcripts: improving precision
TL;DR: Standard NER models trained on clean text lose up to 27 F1 points when applied to raw ASR output. For CCaaS operations running automated QA and CRM sync, that gap translates directly into missed account numbers, corrupted customer records, and unreliable coaching scores. The fix starts at the transcription layer. Our Solaria-1 model delivers lower WER on conversational speech and 3x lower DER than alternatives, giving your NER pipeline a clean text foundation before a single field is written to the CRM.
How to integrate live transcription API with Twilio to transcribe calls in real time
Published on Sep 28, 2023
Twilio, used by hundreds of thousands of businesses and more than ten million developers worldwide, can now integrate with our live transcription API. The integration makes it easier for users to natively transcribe any phone call in real time while using Twilio. With transcribed text at your disposal, you'll then be able to analyze, archive, and act upon voice data more effectively.
Below, you’ll find a step-by-step guide on setting up the Twilio integration with Gladia API in JavaScript for free.
What can you do with Twilio integration?
Any developer can use this integration to transcribe phone calls in real-time.
How to implement Twilio + Gladia real-time transcription integration
Step 1: Set up your Gladia account
If you haven't already, sign up for our Speech-to-Text API at app.gladia.io and obtain your API key.
Step 2: Create and parametrize your Twilio account
Get a phone number, following the first step of the main page to connect to your Twilio account.
On the left panel Develop > United States (US1) > Phone Numbers > Manage > Active numbers.
Click on the phone number you just created.
In 'Configure' panel, 'Voice Configuration' section, 'A call comes in' field, choose 'Webhook' with URL = 'http://[your-id-address]:[your-app-port-number]' and HTTP = 'HTTP POST'
Step 3: Configure your server and install dependencies
In .env file, add GLADIA_API_KEY var with your API key obtained from Gladia’s website and PORT var, the port you used to configure your phone number in above section (default is 8080)
Feel free to check out the video version of the tutorial for a step-by-step walkthrough with one of our software engineers, Antoine.
We hope you enjoyed this how-to tutorial! Given how much audio data still goes to wasted, we’re always curious to explore the many ways in which transcription tech can be used to remedy that. Let us know if you end up using our API with Twilio, Discord, or other, we’d love to hear from you.
About Gladia
At Gladia, we built an optimized version of Whisper in the form of an API, adapted to real-life professional use cases and distinguished by exceptional accuracy, speed, extended multilingual capabilities and state-of-the-art features, including speaker diarization and word-level timestamps.
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Speech-To-Text
Call center voice analytics: use cases, benefits, and how it works
Speech-To-Text
Customer sentiment analysis: methods, tools, and what voice data adds
Speech-To-Text
Named Entity Recognition from call transcripts: improving precision
From audio to knowledge
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