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Introducing Expression APIs: Give your AI more of what people mean
Human communication carries meaning beyond words. A change in tone, a hesitant delivery, or a surprised expression can change how we understand what someone says, and how we respond. Consider “I can’t believe it.” After good news, those words might convey excitement. After a setback, they might express frustration. During a playful exchange, they might communicate amusement. When AI applications hear these words in any of these contexts, the words they transcribe stay exactly the same.
Oct 6, 2026
Behind Hume’s Expression Measurement Models for Face and Voice
Editor's note: This post covers the science, development and evaluation of Hume’s state of the art Expression API, that enables developers to enrich their applications or research pipelines with real-time measurement of voice and facial expressions. Facial expressions and vocal tone are central to how we communicate. They convey emotions such as amusement, interest, frustration, and surprise, often without naming those feelings in words. Understanding these signals is part of how we connect with one another and respond to what others express.
Oct 6, 2026
Evaluating Google’s multi-speaker TTS: A case study in why private evaluations matter
Text-to-speech systems were originally built to read text aloud in a single voice. As voice AI expands into audiobooks, game dialogue, and advertising, these systems are being asked to do more: generate conversations between multiple speakers. That takes more than generating two distinct voices and stitching their lines together. The speakers need to sound like they are responding to one another, with natural timing, changes in tone, and smooth handoffs. Each voice must remain distinct and consistent while contributing to a believable conversation.
Sharath Rao, Kimberly Lo, Alice Baird / Sep 29, 2026
Newly-released Google's Gemini 3.8 Flash TTS tops Hume's Real-World VoiceEQ leaderboard
Voice models are advancing quickly, bringing new capabilities to a growing range of use cases. Evaluating them requires assessing a broader range of qualities—and whether models can sustain their performance over longer conversations and passages. Hume’s Real World VoiceEQ benchmark takes this broader view of performance, measuring the human qualities of speech, including tone, expression, naturalness, speaker identity, and reliability, across real-world conditions and use cases. We recently extended our evaluations to cover voice controllability and voice replication.
Alice Baird / Sep 24, 2026
In The News: Google Announces Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS (opens in a new tab)
Google introduces two new text-to-speech models to the Gemini family. Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS, their most expressive audio generation models yet.
Sep 23, 2026
Introducing the Hume Voice Controllability Leaderboard
New leaderboard tests whether text-to-speech models actually follow direction, finding that for some models the dial doesn't move no matter what you ask.
Hoon Shin, Shahbaz Mogal, Alice Baird / Sep 17, 2026
Introducing the Hume Voice Replication Leaderboard
New leaderboard rates eleven text-to-speech models on voice replication, finding the most natural-sounding clone is often not the right person.
Sharath Rao, Kimberly Lo, Alice Baird / Sep 10, 2026