Voice AI is about to have its ChatGPT moment.

OpenAI's Whisper made speech recognition nearly free. Real-time translation is now possible. Voice cloning requires 3 seconds of audio. Text-to-speech is indistinguishable from human speech.

The applications: meeting transcription and summarization, real-time multilingual customer support, voice-controlled AI assistants that actually work, podcast search (search through hours of audio by topic), accessibility tools for visually impaired users.

The pipeline I build most often: Whisper for speech-to-text, LLM for understanding and processing, edge TTS for text-to-speech response. End-to-end latency under 2 seconds.

What makes voice AI technically interesting: it's a multimodal pipeline where each component's errors compound. A small transcription error leads to a misunderstanding by the LLM, which produces a wrong response, which the TTS delivers confidently.

Error handling and confidence scoring at each stage is critical. If Whisper isn't confident about a transcription, the system should ask the user to repeat, not guess.

If you're looking for a specialization that's about to explode, voice AI is the one. The building blocks are mature. The products are just starting.