Text-independent speaker identification in a distant-talking multi-microphone environment

Abstract

With the aim of improving speaker identification, we propose a likelihood-based integration method to combine the speaker identification results obtained through multiple microphones. In many cases, the composite result has lower error rate than that by any single channel. The proposed integration method can achieve more reliable identification performance in the ubiquitous robot companion (URC) environment in which the robot is connected to a server through an extremely high broadband penetration rate.