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Speech-To-Text

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.

Speech-To-Text

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.

Speech-To-Text

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.

Gladia selected to participate in the 2024 AWS Generative AI Accelerator

Published on Sep 18, 2024
Gladia selected to participate in the 2024 AWS Generative AI Accelerator

We’re proud to announce that Gladia has been selected for the second cohort of the AWS Generative AI Accelerator, a global program offering top early-stage startups that are using generative AI to solve complex challenges, learn go-to-market strategies, and access to mentorship and AWS credits.

This opportunity will help Gladia build, train, test, and launch products such as agent assistance for contact center platforms, sales enablement tools and AI meeting assistants, and enable voice-first platforms to deliver more value to their users across borders.

“The new generation of startups is at the forefront of a transformative new wave, pushing the boundaries of what’s possible with artificial intelligence while bringing exciting new solutions to market,” said Jon Jones, Vice President of Go-to-Market at AWS and executive sponsor of the program.
“Expanding the cohort for our Generative AI Accelerator is a testament to the potential we see for startups to usher in new innovations for customers in an increasingly AI-driven world. AWS is committed to fostering groundbreaking technologies and supporting visionary founders on their journey to solve the world’s biggest challenges.”

Gladia is one of 80 global startups from around the world selected for the program, and we’ll attend and showcase our solutions to potential investors, customers, partners, and AWS leaders in December at re:Invent 2024 in Las Vegas.

For more information on the Generative AI Accelerator, visit AWS Generative AI Accelerator.

About Gladia

Gladia provides a speech-to-text and audio intelligence API for building virtual meeting and note-taking apps, call center platforms, and media products, providing transcription, translation, and insights powered by best-in-class ASR, LLMs, and GenAI models.

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