Gladia

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.

Speech-To-Text

What is Word Error Rate (WER): How it’s calculated, and why it can mislead

Word Error Rate (WER) is a metric that evaluates the performance of ASR systems by analyzing the accuracy of speech-to-text results. WER metric allows developers, scientists, and researchers to assess ASR performance. A lower WER indicates better ASR performance, and vice versa. The assessment allows for optimizing the ASR technologies over time and helps to compare speech-to-text models and providers for commercial use.

Speech-To-Text

Text normalization in speech recognition explained

Speech recognition systems are good at turning audio into words. But the transcripts they produce aren’t always structured in ways that software can reliably work with.

Speech-To-Text

What is speaker diarization?

One of the major obstacles for speech-to-text AI has been identifying individual speakers in a multi-speaker audio stream before transcribing the speech. This is where speaker separation, also known as diarization, comes into play.

Speech-To-Text

How contact center AI improves efficiency: benchmarks and ROI

TL;DR: Manual QA teams review 1–5% of contact center calls; AI-powered platforms can score all of them, but only when the underlying transcript is accurate. WER and DER are the hidden bottlenecks: a wrong name, missed compliance phrase, or misattributed speaker corrupts every downstream system that reads the transcript, from routing and agent assist to post-call summaries and QA scoring. Our Solaria-1 model delivers on average 29% lower WER than alternatives on conversational speech and on average 3x lower DER (diarization error rate), covers 100+ languages including 42 that no other STT API supports, and handles the full audio pipeline (record, transcribe, enrich) in a single API.

Speech-To-Text

How to integrate AI into contact center performance monitoring

TL;DR: Most contact centers manually review only a small fraction of calls, leaving compliance breaches and coaching signals undetected. Scaling to 100% AI QA coverage means choosing between three integration patterns (CCaaS-native tools, add-on API layers, or a custom build), each determined by how well your speech infrastructure handles noisy, multilingual audio. For post-call monitoring, async batch transcription outperforms real-time on accuracy, diarization quality, and cost predictability at scale. The bottleneck is getting a reliable transcript from noisy call center audio, which is where Solaria-1 and all-inclusive per-hour pricing matter most.

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2026 Meeting Assistant Report

Blog

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

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

What is code-switching in speech recognition?

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

What is Word Error Rate (WER): How it’s calculated, and why it can mislead

Text normalization in speech recognition explained

What is speaker diarization?

How contact center AI improves efficiency: benchmarks and ROI

How to integrate AI into contact center performance monitoring

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