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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.

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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.

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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.

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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. 

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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.

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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.

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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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Speech-To-Text

From transcript to actionable notes: Building effective LLM pipelines for meeting intelligence

Build effective LLM pipelines for meeting intelligence using modular stages, async transcription, and JSON schema enforcement.

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Speech-To-Text

Azure Speech Services vs Gladia: Enterprise SLA, data residency & compliance comparison

Azure Speech Services vs Gladia: Compare enterprise SLA, compliance, pricing, and data residency for speech to text infrastructure. Both platforms meet SOC 2 Type 2 and GDPR requirements, but differ on cost structure and integration speed for product teams building at scale.

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