Improving Speaker Identification Robustness to Highly Channel-Degraded Speech Through Multiple System Fusion

MITCH MCLAREN; NICOLAS SCHEFFER; MARTIN GRACIARENA; LUCIANA FERRER; YUN LEI

Vancouver

Congreso; IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2013

IEEE

This article describes our submission to the speaker identi- fication (SID) evaluation for the first phase of the DARPA Robust Audio and Transcription of Speech (RATS) program. The evaluation focuses on speech data heavily degraded by channel effects. We show here how we designed a robust sys- tem using multiple streams of noise-robust features that were combined at a later stage in an i-vector framework. For all channels of interest, our combination strategy presents up to a 41% relative improvement in miss rate at a 4% false alarm rate with respect to the best-performing single-stream system.