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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.
Research engineer Hoon Shin built this liquid metal soundscape with our Audio Decision API. Using the model’s span feature, it picks out and identifies each sound, then renders it as an object morphing in and out of existence.
Voices are read for age and language, while laughter and cheers are read for emotion and intensity, and the louder the room gets, the more the sea moves.
Not a product, just a glimpse of what this model makes possible.
When AI can actually hear, things get a lot more interesting.
Built on the Hume Audio Decision API — request access: hume.ai/products/decision-mo…
For those curious about batch mode: this uncut demo shows the Hume Audio Decision API working through hours of recorded calls, in real time.
Reading this post probably took longer than the processing did.
All your questions. All your audio. Confidence scores on every answer.
Full demo below.
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.
Get started with the Hume Audio Decision API: hume.ai/products/decision-mo…
Get started with the Hume Audio Decision API: hume.ai/products/decision-mo…
@ElevenLabs v4 Turbo and v4 just joined the Real World VoiceEQ leaderboard at 3rd and 4th of 38.
We ran both through 18 of our human evals, three generations per prompt, with native-speaker raters for non-English passages.
Where there's room to grow:
• English long-form sits mid-table.
• Alphanumeric words, like codes and IDs: v4 is 13th of 38.
• Audio tags work on short directions but get read aloud past about ten words, so expressiveness couldn't be fully scored yet. The overall rank covers the questions they could be rated on.
A big step up from v3, which sits 15th.
Full results and the leaderboard: hume.ai/benchmark#tab-tts
Someone says "I'm fine." You know they aren't.
You heard it - the pause, the flat tone. Voice AI doesn't. It all gets dropped at transcription.
So we built the Expression Measurement API. 48+ emotions, 600+ descriptors, 50+ languages, audio and video, real-time.
Conversation was always more than words.
Docs, both SDKs, and the Python and Node quickstarts: dev.hume.ai/expression-measu…
Currently in limited release - contact us for access.
Text-to-speech was built to read a single voice aloud. Now it's being asked to hold a conversation between two, and that's a different problem entirely.
The speakers have to stay distinct, trade turns with natural timing, and keep their emotional states from bleeding into each other. None of that shows up in word error rate.
So we built a rubric: five dimensions, 48 two-speaker scripts, and four scenarios designed to break a dialogue by pairing similar-sounding voices, forcing frequent interruptions, contrasting emotions, and running long exchanges.
Three blind human raters per clip. We ran three generations of Google's Gemini TTS models through it, alongside other multi-speaker systems.
Keeping two voices apart is the hardest part of the job. Every system handles a male and female pair easily and drops on at least one same-gender pairing. The newest Flash model made its biggest gain on exactly that.
Models with the same overall score get there differently. One leads on keeping speakers’ voices distinct, another on making the dialogue sound like one continuous recording. The overall score hides that difference.
New capabilities need new evaluations.
Full case study, with audio examples, is linked in the comments.
3/ Gemini 3.8 Flash and Flash-Lite take the top two spots on Overall, at 0.92 and 0.91.
Gemini 3.1 Flash, an older model, still leads Acting at 3.97, well clear of the 3.69 behind it.
5/ Every model on the board leads on something and trails on something else. That keeps being the finding.
Google published their own Gemini 3.8 TTS results today and cited the board: blog.google/innovation-and-a…
6/ The board on Hugging Face, with the full per-factor breakdown:
huggingface.co/spaces/HumeAI…
Same data on our site, with every factor tab:
hume.ai/rw-voice-eq
