Speech Recognition

Speech Recognition, also called speech-to-text or STT, is technology that converts spoken audio into written text automatically. Modern systems feed audio into a neural model trained on large multilingual speech corpora, which maps sound directly to text instead of relying on hand-built phonetic rules, and this is why accuracy on accents and noisy recordings improved sharply. Useful systems add speaker diarization, which labels who spoke each segment, plus timestamps and punctuation, since raw text without those is hard to use. OpenAI's Whisper is the reference open model and can be self-hosted, while Deepgram and AssemblyAI sell managed APIs tuned for streaming and low latency. The most visible application is meeting transcription: tools such as Otter, Granola, and the recording features in Zoom and Microsoft Teams transcribe a call and then pass the text to a language model for summaries and action items. Voice assistants and call-center analytics use the same stack. The pitfall is assuming a transcript is a record. Recognition still fails on domain jargon, product names, and heavily accented or overlapping speech, so anything consequential needs review. Recording a conversation also carries consent requirements that differ by country.