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



McLaren, M., Scheffer, N., Graciarena, M., Ferrer, L., & Lei, Y. (2013, May). Improving speaker identification robustness to highly channel-degraded speech through multiple system fusion. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6773-6777). IEEE.


This article describes our submission to the speaker identification (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 system 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.

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