[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-speech-to-text::en":3,"gloss-cluster-speech-to-text::en":20,"gloss-next-speech-to-text::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"speech-to-text","output","Speech-to-Text (STT)","Speech-to-text (STT), also called automatic speech recognition (ASR), converts spoken audio into written text. Modern STT systems (OpenAI Whisper, Google Speech-to-Text, AssemblyAI, Deepgram) use end-to-end neural architectures — typically transformer encoder-decoder models — trained on hundreds of thousands of hours of multilingual audio, achieving word error rates under 5% for clear studio audio in major languages and holding up reasonably well against accents, background noise, and overlapping speakers — a substantial jump in robustness compared to the pre-2020 generation of ASR systems that required near-studio-quality audio to perform reliably. Why it matters for SaaS builders: STT is the entry point for any voice-driven product — meeting transcription tools (Otter.ai, Fireflies), voice-controlled apps, call-center analytics, subtitle\u002Fcaption generation, and voice-to-text input fields. It's also a critical preprocessing step for voice AI agents, where STT converts the caller's speech to text, an LLM reasons over that text, and TTS speaks the response back — the classic \"STT → LLM → TTS\" voice-agent pipeline. Integration is typically a single API call: upload an audio file or stream audio in real time and receive a transcript, often with word-level timestamps, speaker diarization (who said what), and confidence scores. A concrete worked example — building a \"meeting notes\" SaaS feature: (1) user uploads a 45-minute Zoom recording (MP3), or the app captures audio live via the Zoom\u002FMeet bot API for real-time processing; (2) the app calls `POST https:\u002F\u002Fapi.deepgram.com\u002Fv1\u002Flisten?model=nova-2&diarize=true&punctuate=true` with the audio file; (3) the response returns a JSON transcript with per-speaker segments, word-level timestamps, and confidence scores, e.g. `{\"speaker\": 0, \"start\": 12.4, \"confidence\": 0.97, \"text\": \"Let's review Q3 targets.\"}`; (4) the app feeds the full transcript into an LLM with the prompt \"Summarize this meeting into action items grouped by owner, and flag any unresolved questions\"; (5) the summary, action-item list, and full searchable transcript are shown side by side, with each action item deep-linking to the exact transcript timestamp it was extracted from so a user can verify context. Key parameters: language\u002Fmodel selection, diarization (speaker separation — critical for multi-person calls but adds processing time and cost), real-time streaming vs. batch processing, and custom vocabulary boosting to improve recognition accuracy on domain jargon like product names or technical acronyms that a general-purpose model would otherwise mishear. Cost is typically billed per audio-minute processed, so high-volume products often pre-filter silence using voice activity detection before sending audio to the paid STT API.","Speech-to-text (STT), or automatic speech recognition, transcribes spoken audio into written text.",null,[11,14,17],{"slug":12,"name":13},"summarization","Summarization",{"slug":15,"name":16},"text-to-speech","Text-to-Speech (TTS)",{"slug":18,"name":19},"transcription","Transcription",[21,25,29,33,36,40,43,46,49,52,55,58],{"slug":22,"category":5,"name":23,"updated_at":24},"abstention","Abstention","2026-08-24T03:30:02+00:00",{"slug":26,"category":5,"name":27,"updated_at":28},"ai-copywriting","AI Copywriting","2026-08-24T02:46:38+00:00",{"slug":30,"category":5,"name":31,"updated_at":32},"ai-watermarking","AI Watermarking","2026-08-24T02:46:37+00:00",{"slug":34,"category":5,"name":35,"updated_at":32},"aspect-ratio-control","Aspect-Ratio Control",{"slug":37,"category":5,"name":38,"updated_at":39},"audio-generation","Audio Generation","2026-08-24T02:46:36+00:00",{"slug":41,"category":5,"name":42,"updated_at":32},"audio-super-resolution","Audio Super-Resolution",{"slug":44,"category":5,"name":45,"updated_at":39},"avatar-generation","Avatar Generation",{"slug":47,"category":5,"name":48,"updated_at":39},"background-removal","Background Removal",{"slug":50,"category":5,"name":51,"updated_at":32},"batch-image-generation","Batch Image Generation",{"slug":53,"category":5,"name":54,"updated_at":28},"brand-voice","Brand Voice",{"slug":56,"category":5,"name":57,"updated_at":28},"cfg-scale","CFG Scale (Classifier-Free Guidance)",{"slug":59,"category":5,"name":60,"updated_at":32},"character-consistency","Character Consistency"]