Real-Time Voice Dictation Solutions

Last weekend, I crashed my bike and broke my dominant elbow. Typing one-handed, hunt-and-peck style wasn’t going to cut it — so I started testing modern speech-to-text tools. A lot has changed... Seeing the rise of "vibe coding" — people building entire programs just by talking to AI made me wonder: are we finally at the point where we can do real, complex work purely by voice? This injury gave me the push to find out. Here’s what I’ve found so far: 🗣️ ChatGPT + Dictation (desktop or mobile interface) • Click the mic icon, speak, and lightly edit if needed. • Surprisingly good at punctuation, flow, and formatting. • Great for refining and tightening text before pasting into emails, documents, or anywhere else. • (By contrast, the dynamic speech mode is more conversational and less suited for deep work — no visible text.) 🖥️ Windows Voice Access • Built into Windows 11. • Manual activation often required; voice triggers are unreliable. • Handles direct input into any app — Slack, email, browser — no extra steps needed. • Punctuation, new lines, and capitalization need improvement, but it’s convenient for quick, rough entries. 📱 Apple iOS Dictation • Fast, intuitive, and extremely accurate — surprisingly on par with GPT dictation. • Works great inside mobile apps like Slack, Notes, and Mail. • Limited to mobile, but fantastic for short-form productivity & communications. Overall: speech-to-text is getting seriously good — especially when paired with AI that can clean up rough inputs. We're closer than ever to voice-driven workflows moving beyond accessibility into mainstream productivity. Right now, I’m mainly using ChatGPT Dictation, Windows Voice Access, and Apple iOS Dictation — and planning to explore Whisper API and Google Voice Typing next. 👉 Have you used speech-to-text seriously in your workflow yet? 👉 Which tools (or hacks) have made it actually work for you? Would love your suggestions — planning to pull these learnings into a deeper review soon! P.S. You might have guessed — this post itself was drafted with the help of GPT. I spoke the original ideas, and we iterated in conversation to refine it. Worked pretty well.

Here’s how we built a low-latency, real-time voice assistant without breaking the bank on speech-to-text APIs We were working on a client project that needed a voice-to-voice onboarding experience for a B2B SaaS product. The user would speak with an AI assistant for 5 to 7 minutes, and by the end of it, they’d be onboarded. Naturally, we considered OpenAI’s real-time voice streaming. Also looked at Twilio and Bland. But here’s why we didn’t go ahead with any of them: Cost. - Even on the conservative side, OpenAI would’ve cost $1.2 to $1.5 per minute. That’s $5 to $7 per user session. - At scale, that’s $60 to $80 per hour. Way too expensive for B2C. Still high for B2B. We didn’t need a true voice-to-voice API. What we really needed was a simple loop: Speech to text → Send to LLM → Get response → Convert back to speech. So here’s what we built instead: 1. Used Aanyang, a super underrated browser library that uses Chrome’s built-in speech-to-text 2. For mobile, used Apple’s local speech-to-text APIs 3. Passed the text to a standard LLM like OpenAI or LLaMA 4. Used 11Labs for text-to-speech 5. Streamed the audio response back to the user No real-time streaming. No server-side audio processing. Just simple, local input handling and fast response. This setup cut costs significantly and reduced latency. No need to send audio to the server, and no third-party dependency for streaming. Plus, it gives us the flexibility to self-host text-to-speech in the future and bring costs down even further. That’s how we replaced a $7 onboarding session with something faster, cheaper, and easier to scale. If you're building voice AI experiences, this might be worth trying. Happy to jam on infra, architecture, or anything voice-related. #VoiceAI #LLM #SaaS #AIInfrastructure #TechStack #OpenAI #StartupEngineering #Ionio #FoundersBuil

Voice AI is more than just plugging in an LLM. It's an orchestration challenge involving complex AI coordination across STT, TTS and LLMs, low-latency processing, and context & integration with external systems and tools. Let's start with the basics: ---- Real-time Transcription (STT) Low-latency transcription (<200ms) from providers like Deepgram ensures real-time responsiveness. ---- Voice Activity Detection (VAD) Essential for handling human interruptions smoothly, with tools such as WebRTC VAD or LiveKit Turn Detection ---- Language Model Integration (LLM) Select your reasoning engine carefully—GPT-4 for reliability, Claude for nuanced conversations, or Llama 3 for flexibility and open-source options. ---- Real-Time Text-to-Speech (TTS) Natural-sounding speech from providers like Eleven Labs, Cartesia or Play.ht enhances user experience. ---- Contextual Noise Filtering Implement custom noise-cancellation models to effectively isolate speech from real-world background noise (TV, traffic, family chatter). ---- Infrastructure & Scalability Deploy on infrastructure designed for low-latency, real-time scaling (WebSockets, Kubernetes, cloud infrastructure from AWS/Azure/GCP). ---- Observability & Iterative Improvement Continuous improvement through monitoring tools like Prometheus, Grafana, and OpenTelemetry ensures stable and reliable voice agents. 📍You can assemble this stack yourself or streamline the entire process using integrated API-first platforms like Vapi. Check it out here ➡️https://bit.ly/4bOgYLh What do you think? How will voice AI tech stacks evolve from here?

Voice technology is transforming how we interact with machines, making conversations with AI feel more natural than ever before. With the public beta release of the Voice Live API developers now have the tools to create low-latency, multimodal voice experiences in their apps, opening up endless possibilities for innovation. Gone are the days when building a voice bot required stitching together multiple models for transcription, inference, and text-to-speech conversion. With the Realtime API, developers can now streamline the entire process with a single API call, enabling fluid, natural speech-to-speech conversations. This is a game-changer for industries like customer support, education, and real-time language translation, where fast, seamless interactions are crucial. The Voice Live API is a solution enabling low-latency, high-quality speech to speech interactions for voice agents. The API is designed for developers seeking scalable and efficient voice-driven experiences as it eliminates the need to manually orchestrate multiple components. By integrating speech recognition, generative AI, and text to speech functionalities into a single, unified interface, it provides an end-to-end solution for creating seamless experiences. Blog Link: https://lnkd.in/dCTsKM8x Github link: https://lnkd.in/dYuBvCzZ