Blog

Technical guides, customer stories, and product updates
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Speech-To-Text

OpenAI Whisper API vs. Gladia: A technical comparison for production speech-to-text

OpenAI's Whisper changed what developers expected from speech recognition when it launched as open-source in 2022, and the managed API it powers remains a credible choice for batch English transcription.

Speech-To-Text

How to build an AI note-taker: complete architecture guide with async transcription and LLM integration

Build an AI note taker with async transcription, LLM integration, and full audio intelligence in a single API call with no add-on fees.

Speech-To-Text

ElevenLabs vs Gladia: speech-to-text comparison for voice AI builders

ElevenLabs vs Gladia comparison for voice AI builders. Compare STT accuracy, latency, pricing, and features for production agents. Get real-world accuracy metrics, total cost models, and technical specs to evaluate whether unified vendor stack or best-of-breed STT fits your pipeline.

Speech-To-Text

Meeting bot speech recognition: how real-time transcription powers automated meeting assistants

For developers, the hard part of building a meeting bot isn't the LLM prompt that generates the summary. It's everything before it: capturing raw audio from conferencing platforms whose APIs were not originally designed for continuous data streaming pipelines, splitting that stream by speaker in real time, handling the moment someone switches from English to French mid-sentence, and doing all of it in under 300 milliseconds so the bot doesn't feel broken.

Speech-To-Text

Meeting transcription common mistakes: what meeting assistant builders get wrong

Meeting transcription mistakes that break production systems: crosstalk handling, diarization failures, and code switching issues. Learn how to architect STT pipelines that survive real world audio conditions, avoid silent WebSocket failures, and prevent cost model surprises at scale.

Speech-To-Text

Code-switching in contact centers: why customer calls fail transcription

Code-switching in contact centers causes transcription failures that inflate AHT, create compliance gaps, and break AI tools. Native multilingual models handle language transitions without routing overhead, eliminating accuracy drops that cost you hours in manual rework and hidden compliance risk.

Speech-To-Text

Multilingual meeting transcription: language coverage, accuracy, and code-switching challenges

Multilingual meeting transcription requires testing code-switching, accented speech, and diarization on real audio before committing. Standard WER benchmarks degrade 2.8 to 5.7x in production, so evaluate APIs on your own noisy meeting recordings to avoid user churn from accuracy failures.

Speech-To-Text

What is code-switching in speech recognition?

Code-switching in speech recognition is language alternation within utterances that breaks monolingual ASR models at switch points. End-to-end multilingual architectures handle intra-sentential switches natively without LID routing overhead, reducing WER by up to 55% at language boundaries.

Speech-To-Text

STT API benchmarks: How to measure accuracy, latency, and real-world performance

Benchmarking STT APIs in 2026 requires more than WER. Learn how to evaluate STT APIs using latency, diarization, and real-world conditions in 2026.