RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems
Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes s
https://arxiv.org/abs/2607.14846v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
None
How close the company’s idea is to your thesis statement
Sector
ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder → stable
Traction → stable
Idea vs market ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
D
David Ayllon
1.0
low confidence
A
Alice Baird
2.0
low confidence
J
Jeffrey Brooks
1.0
low confidence
F
Franc Camps-Febrer
2.0
low confidence
J
Jakub Piotr Cłapa
2.0
low confidence
T
Theo Lebryk
2.0
low confidence
J
Jens Madsen
2.0
low confidence
O
Olya Ossipova
2.0
low confidence
S
Sharath Rao
2.0
low confidence
H
Hoon Shin
2.0
low confidence
T
Tigran Soghbatyan
2.0
low confidence
G
Georg Streich
2.0
low confidence
D
David Ayllón
2.6
low confidence
J
J Brooks
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Voicery
· Inactive
Automated voice acting and emotive speech synthesis with deep neural…
founders not scraped yet