Speech-to-Text

Speech-to-Text (STT) AI converts spoken audio into written text — also called Automatic Speech Recognition (ASR). The field has matured dramatically with models like OpenAI's Whisper (open weights, multilingual, handling 90+ languages), Google's USM, Microsoft's Azure Speech, AssemblyAI, and Deepgram. Modern STT systems handle accented speech, technical jargon, multi-speaker conversations (with diarization), and real-time streaming. Applications include meeting transcription (Zoom, Microsoft Teams, Otter), accessibility tools (live captions, hearing-aid integration), voice assistants (Alexa, Siri, Google Assistant), customer-service call analysis, medical dictation (Nuance/DAX), and legal deposition transcripts. Because speech data is highly personal — voices identify speakers and often reveal emotional state, health conditions, and location context — AI governance frameworks treat speech-to-text systems as sensitive AI assets requiring AI compliance, privacy review, and AI risk management. HIPAA in healthcare and GDPR in Europe impose specific obligations on processing voice data as part of responsible AI.

Centralpoint Treats Voice Data as Sensitive — Because It Is: Oxcyon's Centralpoint AI Governance Platform keeps prompts, skills, and audio-derived outputs on-premise. The model-agnostic platform supports ChatGPT, Gemini, Llama, and embedded models, meters consumption, and embeds speech-aware chatbots into your portals with a single JavaScript line.


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