Improvements to the IBM speech activity detection system for the DARPA RATS program

Abstract

In this paper we describe improvements to the IBM speech activity detection (SAD) system for the third phase of the DARPA RATS program. The progress during this final phase comes from jointly training convolutional and regular deep neural networks with rich time-frequency representations of speech. With these additions, the phase 3 system reduces the equal error rate (EER) significantly on both of the program's development sets (relative improvements of 20% on dev1 and 7% on dev2) compared to an earlier phase 2 system. For the final program evaluation, the newly developed system also performs well past the program target of 3% Pmiss at 1% Pfa with a performance of 1.2% Pmiss at 1% Pfa and 0.3% Pfa at 3% Pmiss.