Custom vocabulary support in speech-to-text: how to teach the model your terms

TL;DR: Runtime vocabulary lists suit dynamic, frequently changing term sets; model fine-tuning is justified only for stable, fixed-domain vocabularies with unique acoustic conditions. The critical constraint is list length: adding terms that don't appear in your audio expands the decoder's search space and can degrade accuracy on entries that custom_spelling_config were already transcribed correctly. Measure improvement using keyword error rate on your specific entity set, not a global accuracy score, which masks failures on high-value terms. Update your vocabulary list on the same cadence as your product release cycle and prune any entries Solaria-1 already handles correctly.

Multilingual customer support: scaling global CX with real-time translation and transcription

TL;DR: Scaling global customer support without blowing out cost-per-contact requires more than a translation engine bolted onto a fragile stack. The real constraint is transcription accuracy: every mistranscribed word corrupts the downstream CRM entry, QA scorecard, and coaching output your operation depends on. Offshore BPO staffing can reduce costs by up to 65% compared to onshore agents, but only if your audio infrastructure handles accented speech and mid-call code-switching. This article breaks down the staffing-versus-technology trade-off, when AI translation holds up in production, and what the audio foundation needs to deliver.

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.

Call center automation: benefits, use cases, and how AI works

TL;DR: Call center automation drives measurable cost reduction across QA, wrap-up time, and routing, but that ceiling is set entirely by the accuracy of your transcription layer. When the speech-to-text layer misreads a name, a number, or a compliance disclosure, every downstream system, from automated QA to CRM logging to coaching scorecards, inherits that error silently. This playbook covers the full call lifecycle: where to deploy AI, how to model ROI, what to automate versus keep human, and why multilingual transcription accuracy determines whether your automation investment holds up in production.

Call center analytics: the complete guide to metrics and KPIs

TL;DR: Most contact centers review fewer than 2% of their calls manually, leaving the rest as operational blind spots. Call center analytics closes that gap, but only if the underlying transcription layer is accurate enough to feed QA scorecards, CRM workflows, and coaching systems with reliable data. This guide covers the operational, agent, CX, and voice analytics metric categories operations leaders track, and explains why transcript accuracy, specifically WER and DER on conversational speech, sets the ceiling on everything downstream.

Real-time vs async transcription for contact centers: When streaming is worth the cost

TL;DR: The decision between real-time and asynchronous transcription is not a latency question; it is an architectural fit question. Treating async batch processing as a slower version of streaming misunderstands how both modes work and which workflows each actually serves. Most contact center workloads (post-call QA scoring, conversation intelligence, CRM enrichment, and compliance archiving) belong on async batch transcription, which accesses full conversational context, delivers lower Word Error Rates, and costs 20% less per hour than streaming. Reserve real-time WebSocket streaming for the narrow set of live-call use cases where sub-300ms latency is a functional requirement: live agent assist, IVR routing, and voice agents. Both modes are available through the same platform, so the choice is about fit and cost, not vendor switching.

How WER affects conversation intelligence and agent coaching

TL;DR: Word Error Rate (WER) is the accuracy ceiling for every downstream feature including sentiment scoring, CRM enrichment, and compliance triggers. A 5% WER on a 5-minute call produces roughly 38 incorrect words, concentrated on product names, customer names, and compliance phrases your conversation intelligence stack depends on.