Speech to Text

Speech-to-text converts spoken audio into written text that can be stored, searched, analyzed, or fed into downstream AI workflows. Modern systems are fast and accurate under good conditions: clear audio, standard accents, minimal background noise, familiar vocabulary. Accuracy degrades meaningfully in real-world conditions — heavy accents, overlapping speakers, domain-specific terminology, noisy call center environments. The gap between benchmark performance and production performance is wider for speech-to-text than for most AI capabilities, which makes pre-deployment testing with representative audio essential.

Recording, transcribing, and processing conversations creates data and consent obligations that are easy to underestimate. Depending on jurisdiction, recording a call or meeting may require active consent from all parties, not just a disclaimer. That transcribed data may contain sensitive personal information, health information, or confidential business content — which needs to be classified, retained, and protected accordingly. Organizations deploying speech-to-text in customer interactions often discover post-deployment that their consent processes were insufficient or that their data handling didn't account for the sensitivity of what the transcripts contain. These are legal and compliance issues, not technical ones.