Working paper

Attorney Voice and the U.S. Supreme Court

Daniel L. Chen

Abstract

Using data from 1946–2014, we show that audio features of lawyers’ introductory statements improve the performance of the best prediction models of Supreme Court outcomes. We infer voice attributes using a 15-year sample of human-labeled Supreme Court advocate voices. Audio features improved prediction of case outcomes by 1.1 percentage points. Lawyer traits receive approximately half the weight of the most important feature from the models without audio features.

Replaced by

Daniel L. Chen, Attorney Voice and the U.S. Supreme Court, Computational Analysis of Law, 2019, forthcoming.

See also

Published in

IAST working paper, n. 18-91, December 2018