Speech Activity Detection under Adverse Noisy Conditions at Low SNRs

Abstract

Speech originating from the noisy environments degrades the speech quality and intelligibility, thus reducing the human perceived Quality of Experience (QoE). For example, surveillance using drone during natural catastrophe needs an efficient speech recognition device to recognise the speech of the frozen human in presence of drone noise to save their life. Therefore, it often requires to pre-process the noisy speech in order to reduce the noise artifacts and enhance the speech. This paper detects the speech activity using Voice Activity Detection (VAD). The VAD distinguishes speech activity (speech presence) and speech inactivity (silence/noise) by extracting the speech features and comparing to a threshold. The energy and spectral centroid features are deployed to design VADs. Noisy dataset consisting of urban noise, for example, drone, helicopter, airplane and station noise, is created at different signal-to-noise ratios (SNRs). F-score and Euclidean distance are used to measure the performance of VADs. Results demonstrate that the spectral centroid VAD performs outstanding with various noise degradations tested.