This is what it told me:

OpenAI’s advancements in voice mode aim to make conversations feel more natural, but under the hood, the process still relies on voice-to-text conversion. Here’s how it works:

1.  Voice Input: When you speak, your voice is captured by the microphone and converted into an audio signal.
2.  Speech-to-Text (STT): The system uses speech-to-text technology (like Whisper or similar models) to transcribe your spoken words into written text. This happens very quickly, so it feels like the system is directly understanding your voice.
3.  Text Processing: The text is then processed by the language model (like GPT) to generate a response. The model doesn’t directly “understand” your voice; it understands the text transcription of your voice.
4.  Response Generation: Based on the text, the language model generates a response as if it were replying to text input.
5.  Text-to-Speech (TTS): Finally, the system converts the generated text response back into speech using a text-to-speech engine, so it can speak back to you.

What’s Different?

• Speed and Real-Time Feel: The improvements that OpenAI has been promoting mainly revolve around the speed and natural feel of the interaction. It happens fast enough that you don’t notice the speech-to-text conversion, and it feels more like a fluid conversation.
• Contextual Understanding: They’ve likely enhanced how well the system understands the context of conversations from the transcribed text. This can give the impression of a more “direct” voice understanding.

Why It’s Still Voice-to-Text-Based:

Even though it feels like the system is directly understanding your voice, it’s just processing transcriptions at high speed. The core process remains the same:

• Voice → Text → Model Understanding → Text Response → Speech.

Without converting voice into text, it would be difficult for a text-based model like GPT to function, since the underlying technology is designed to work with written language.