Improved voice activity detection based on a smoothed statistical likelihood ratio

This paper presents the behavioural mechanism of a statistical model-based voice activity detector (VAD), featuring a likelihood ratio test for the activity decision. From investigation of the VAD, it is found that detection errors could occur frequently at speech offset regions because of the delay term in the decision-directed parameter estimator, employed for the estimation of an unknown parameter of the likelihood ratio. Hence, this paper proposes a smoothed likelihood ratio so as to alleviate the detection errors at the offset region. Objective test results show that the proposed scheme is useful for achieving a considerable performance improvement for the VAD. Additionally, the proposed VAD gives detection performances superior to G.729B VAD and comparable with the AMR VAD option 2