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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.

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.

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: