AI voices can already sound startlingly human. The harder problem is whether they actually understand how humans speak.

Andrew Ettinger, CEO of Hume AI, joins Corey Noles and Grant Harvey to explain why voice AI still has a “listening problem.” A transcript can capture the words someone says while stripping away tone, hesitation, frustration, background noise, dialect, speaker identity, and the other signals that shape what those words actually mean.

The conversation explores how Hume evaluates voice systems against real-world conditions, why emotion is better understood as a high-dimensional spectrum than a handful of neat labels, what breaks during long-running voice interactions, how speech-to-speech systems and AI voice agents could become a primary interface for computing, and what new safety and personalization challenges appear when AI starts talking back.

If you want to understand what has to improve before voice AI feels truly natural—not just realistic—this episode is a practical look at the infrastructure, evaluation, and human feedback behind the next generation of spoken AI.

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How Hume AI Is Teaching Voice Systems to Understand More Than Words

The Neuron: AI Explained

How Hume AI Is Teaching Voice Systems to Understand More Than Words