Hume AI ✓

@hume_ai · New York, NY · joined 09 Mar 2021

Build and measure voice models and agents the way people experience them, based on research and grounded in real human judgment.

23 213Followers

100Following

729Posts total

320.6KViews on collected posts

Postingan terbaru

Hume AI ✓@hume_ai14 Sep

Congratulations to the @OpenAI team on the GPT-Live-1 launch. Here is the early read from our RWVoiceEQ STS evals. Voice naturalness is where it moves: up about 70% on their previous flagship, from near the bottom to level with Gemini 3.1 Flash Live at the top. What was 6.8K views · 67 likes · 3 reposts · 8 replies Open on X →

Hume AI ✓@hume_ai10 Sep

We asked 11 voice cloning models to copy the same 25 voices, then asked real people: is that the same person? The most natural-sounding clone ranked 8th of 11 on sounding like the person. New from Hume AI: the Voice Replication Leaderboard. Live now on @huggingface, links https

14.9K views · 80 likes · 18 reposts · 3 replies Open on X →

Hume AI ✓@hume_ai2 Sep

Sounding human is the third most important factor in voice AI. Not the first. Our CEO Andrew Ettinger (@ahe23) joined the MTS podcast (@MTSlive) to break down CLEAR, the framework we use internally to grade voice AI: 1. Can it respond appropriately? If not, nothing else https:/

1:16

14.5K views · 23 likes · 2 reposts · 2 replies Open on X →

Hume AI ✓@hume_ai22 Aug

For anyone who saved the paper and hasn't opened it :) https://t.co/rGpdrX8njs 3.5K views · 19 likes · 3 reposts · 0 replies Open on X →

Hume AI ✓@hume_ai21 Aug

We silenced a number in the benchmark audio. It was never spoken. Some of the highest-scoring speech recognition models wrote it down anyway, in 30–40% of cases. New research with @huggingface on measuring when a model is optimizing for the test instead of the audio. https://t.

34.9K views · 78 likes · 11 reposts · 11 replies Open on X →

Hume AI ✓@hume_ai15 Jul

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. https://t.co/wMYUeAtWyI

2:23

245.9K views · 156 likes · 43 reposts · 21 replies Open on X →

Dibandingkan akun berukuran sama

5 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. tampil luas, tetapi sedikit penonton yang merespons.

Median tayangan14 524akun ini924median untuk 10K–100K

Jangkauan, %62.57%akun ini3.62%median untuk 10K–100K

Interaksi, %0.63%akun ini1.52%median untuk 10K–100K

Metrik	Akun ini	Median untuk 10K–100K	Rasio
Median tayangan per postingan	14 524	924	15.7×
Jangkauan (tayangan ÷ pengikut)	62.57%	3.62%	17.3×
Tingkat interaksi	0.63%	1.52%	0.41×

Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

245.9K15 Jul

34.9K21 Aug

3.5K22 Aug

14.5K2 Sep

14.9K10 Sep

6.8K14 Sep

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.09%15 Jul

0.29%21 Aug

0.63%22 Aug

0.19%2 Sep

0.68%10 Sep

1.15%14 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes53.6%423 in total

Reposts10.1%80 in total

Replies5.7%45 in total

Bookmarks30.5%241 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.