Environmentally aware voice activity detector

Abstract

Abstract Traditional voice activity detectors (VADs) tend to be deaf to theacoustical background noise, as they (i) utilize a single operatingpoint for all SNRs (signal-to-noise ratios) and noise types, and(ii) attempt to learn the background noise model online from fi-nite data length. In this paper, we address the aforementionedissues by designing an environmentally aware (EA) VAD. TheEA VAD scheme builds prior offline knowledge of commonlyencountered acoustical backgrounds, and also combines the re-cently proposed competitive Neyman-Pearson (CNP) VAD witha SVM (support vector machine) based noise classifier. In oper-ation, the EA VAD obtains accurate noise models of the acous-tical background by employing the noise classifier and its priorknowledge of the noise type, and thereafter uses this informa-tion to set the best operating point and initialization parametersfor the CNP VAD. The superior performance of the EA VADscheme over the standard AMR (adaptive multi-rate) VADs inlow SNR is confirmed in a simulation study, where speech andnoise data were drawn from the SWITCHBOARD and NOISEXdatabases. We report an absolute improvement of 10-15% in de-tection rates over AMR VADs in low SNR for different noisetypes.Index Terms: noise modeling, voice activity detector, environ-mental sniffing