Introducing the Hume Audio Decision API. Ask anything: How many speakers? Is anyone laughing? Is the caller frustrated? What used to take 10+ models is now one call. Trained on 10M+ human judgments, so it answers the way human listeners would. Realtime, or across hours of recordings. Request access below.

Today Hume is introducing Real World VoiceEQ: a benchmark for measuring the human quality of voice AI across 40+ proprietary and open-source models, 15+ dimensions, and 60+ metrics—developed from more than 1 million individual human ratings.

Today, we're announcing significant new traction in our voice AI research and development partnerships. Over the past few months, we’ve quietly been expanding our focus on providing frontier voice AI training data to research labs looking to imbue emotion understanding and cutting edge expressivity into their foundation models. We believe some of the most exciting possibilities for deeper speech and emotion understanding will come to fruition this year. By training today’s frontier models to understand the nuances voice interaction—rife with subtle tones of frustration or satisfaction, “aha” moments, chuckles, sighs, backchannels, and interruptions—we believe that labs will unlock new possibilities for voice AI to become a primary interface within many applications. In our experience working with leading research labs and AI-first enterprises, a consistent pattern we’ve seen is a greater need for high-quality datasets and evaluation pipelines than for new algorithms or architectures. Researchers spend up to 80% of their time curating the data they need to diagnose model issues, fix failure modes, and improve model behavior. That’s why Hume is now focused on building the data and evaluation infrastructure needed to train next-generation voice models across industry.  With the right training, we hope that deeper voice understanding and empathy can be translated not just into more efficient interfaces but into better alignment of AI with human well-being. Read more below