Adaptive high accuracy approaches to speech activity detection in noisy and hostile audio environments

This study examines the difficult task of Speech Activity Detection (SAD) in two hostile environments: AM push-to-talk air traffic control and international telephone conversations with very low SNRs. Due to the poor performance of traditional energy-based SAD, two novel approaches to SAD were developed that specifically target spectral characteristics that typify speech, rather than trying to separate out the background, which can vary enormously. As a result these approaches are inherently adaptive to their environments. A Speech Energy Resonance Band Detection approach and a Harmonic Product Spectrum clustering approach to SAD are described in this paper and their performance evaluated against MIT Xtalk and the Teager Energy Operator (TEO) in clean and hostile environments.