Voice AI needs timing, not just words
Turning speech into text is no longer the main hard part of voice AI. Tools like Whisper have made good-enough cheap and widely available. The harder problem is the information around the words.
Who spoke when, whether two people talked over each other, and when a short response like “mhm” happened can change the meaning of a conversation. Without timing, it is hard to detect interruptions or tell active listening from noise. A that only sees a plain can miss the difference between a calm discussion and a heated exchange, because both may look similar as text.
Stress and tone can also change what a sentence means, so voice AI loses important context when it keeps only the words.
Key points
- is much cheaper and easier than it used to be because of tools like Whisper.
- Timing can show interruptions, overlap, and whether a listener is following along.
- A plain can hide the difference between polite turn-taking and people talking over each other.
- Stress and tone can change the meaning of spoken words.
- may need compact speaker and timing data, even when trying to reduce token use.