OpenAI Whisper vs Google STT vs Amazon Transcribe: the ASR rundown (2026 edition)

TL;DR: OpenAI's gpt-4o-transcribe, Google's Chirp 3, and Amazon Transcribe have each closed real gaps since earlier versions, but they specialize differently. OpenAI leads on clean-audio accuracy, Google on language breadth and native diarization, Amazon on call-center and medical vertical tooling. Published WER numbers barely separate the three on clean, single-speaker audio anymore, so the deciding factor is usually what's already in your stack and what use case you're solving for. The real gap shows up on conversational, multi-speaker, accented, or code-switched audio, conditions the standard benchmark tables don't test. We built Solaria-1 specifically for multi-speaker, multilingual, real-world audio with native code-switching, and Solaria-3, our newest speech model for real-world business audio.

MiFID II and FCA call recording: compliance for voice transcription in finance

TL;DR: Financial firms operating under MiFID II and FCA jurisdictions must maintain searchable, high-accuracy records of all client-related voice communications, including remote and mobile calls under FCA Market Watch 66. Engineering and product teams building for these obligations typically implement dedicated cloud infrastructure with defined data residency, controls that prevent customer audio from being used to retrain models on Growth and Enterprise plans, and transcription accurate enough that the resulting records hold up under regulatory review. Transcription and speaker attribution errors are not product quality issues in this context. They are audit trail failures, and regulators treat them as such.

Why speech-to-text accuracy matters upstream of your LLM

TL;DR: Downstream LLM performance is ceiling-bounded by upstream transcription accuracy. A transcript with a meaningful error rate doesn't produce proportionally degraded summaries or CRM entries. It produces outputs where hallucinated names, inverted logic, and misattributed speaker turns compound silently into every downstream system that reads them. Prompt engineering cannot recover information the STT layer never captured. Gravite cut call quality review time by 93%, from 15 minutes to 1 minute per call, once transcript accuracy was high enough to trust the output without manual verification.

Telephony-audio robustness: why 8kHz narrowband calls break generic STT

TL;DR: Telephony audio is constrained to 8kHz narrowband frequencies, stripping away the high-frequency spectral energy that generic 16kHz STT models require. Standard upsampling cannot recover phonemes that were never captured at the source, leading to transcription errors that compound silently into downstream NLU failures. Solaria-3 ranks #1 on Switchboard, the most challenging conversational telephony dataset, ahead of AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics, ensuring your downstream LLM pipelines receive clean, structured data from real-world noisy call audio.

Gladia to join OVH Groupe, accelerating Europe's sovereign voice AI ambitions

PARIS, France — Sept 21, 2026 — Gladia, the AI audio infrastructure provider trusted by more than 300,000 developers and 2,000 enterprise customers worldwide, announced July 31st, that it has been acquired by OVH Groupe, the parent company of OVHcloud and Europe's leading sovereign cloud provider.

Migrating from Azure Speech to Gladia: a step-by-step switching guide

TL;DR: Migrating from Azure Speech to Gladia removes the overhead of custom training pipelines and fragmented per-feature billing. Azure routes diarization, translation, and sentiment through separate services with separate billing meters. We bundle all audio intelligence into one per-hour rate on Starter and Growth plans. Solaria-3 ranks #1 for real-world European business audio, Solaria-1 covers 100+ languages with native code-switching. Both deliver out-of-the-box accuracy that eliminates custom training for most production audio. Most engineering teams complete the API refactoring in under 24 hours.

How to evaluate a speech-to-text API: a technical buyer's framework

TL;DR: Choosing an STT API on vendor benchmarks alone is how teams end up with transcription that looks fine in staging and breaks on production audio. A rigorous evaluation requires building a test set from your own calls, measuring word error rate (WER) on your specific audio distribution, stress-testing latency under concurrent load, and auditing data retraining terms before signing. This guide gives you a reusable engineering blueprint to run that evaluation end-to-end, the same methodology behind our own open async benchmark, which covers 7 datasets and 74+ hours of audio across 8 providers.

The contact center QA scorecard: what to measure and how transcription feeds it

TL;DR: Manual QA teams review as little as 1% to 2% of contact center calls, leaving the vast majority of interactions unreviewed and exposing systemic compliance risks that sampling never surfaces. Scaling to automated coverage requires transcription accurate enough to power LLM-based scoring without silent failures. If your speech-to-text engine misattributes a speaker or drops a compliance disclosure, every downstream scorecard, CRM entry, and coaching flag is wrong. French CCaaS platform Gravite cut per-call review time from 15 minutes to 1 minute (93% reduction) while automating coverage across their full 50,000 hours of annual call volume on infrastructure built for real-world contact center audio.

HDS and French healthcare data residency for voice transcription

TL;DR: Standard GDPR compliance does not satisfy French law for patient voice data. Article L.1111-8 of the French Public Health Code mandates HDS certification for every subcontractor in your transcription pipeline, and the May 16, 2026 HDS v2.0 transition deadline has invalidated all v1.1 certificates. We provide HDS-certified speech-to-text on dedicated EU cloud clusters, combining strict compliance with Solaria-3, our model built for noisy, accented European business audio and validated for clinical use with custom vocabulary support. This guide covers legal requirements, data residency boundaries, and a procurement checklist for compliant voice pipelines.