Robust speech activity detection using LDA applied to FF parameters

Speech detection becomes more complicated when performed in noisy and reverberant environments like e.g. smart rooms. In this work, we design a robust speech activity detection (SAD) algorithm and we evaluate it on distant microphone signals acquired in a smart room-like environment. The algorithm is based on a measure obtained from applying linear discriminant analysis (LDA) on frequency filtering (FF) features. With a time sequence of this measure, a decision tree based speech/non-speech classifier is trained. The proposed SAD system is evaluated together with other SAD systems (GSM SAD and ETSI advanced front-end standard SAD) using a set of general SAD metrics as well as using the ASR accuracy as a metric. The proposed SAD algorithm shows better average results than the other tested SAD systems for both the set of general SAD metrics and the ASR performance.