Exploiting contextual information for speech/non-speech detection

In this paper, we investigate the effect of temporal context for speech/non-speech detection (SND). It is shown that even a simple feature such as full-band energy, when employed with a large-enough context, shows promise for further investigation. Experimental evaluations on the test data set, with a state-of-the-art multi-layer perceptron based SND system and a simple energy threshold based SND method, using the F-measure, show an absolute performance gain of $4.4\%$ and $5.4\%$ respectively. The optimal contextual length was found to be 1000 ms. Further numerical optimizations yield an improvement ($3.37\%$ absolute), resulting in an absolute gain of $7.77\%$ and $8.77\%$ over the MLP based and energy based methods respectively. ROC based performance evaluation also reveals promising performance for the proposed method, particularly in low SNR conditions.