On The Role of Room Acoustics in Audio Presentation Attack Detection

Abstract

Presentation attack detection (PAD) aims to determine if a speech signal observed at a microphone was produced by a live talker or if it was replayed through a loudspeaker. This is an important problem to address for secure human-computer voice interactions. One characteristic of presentation attacks where recording and replay occur within enclosed reverberant environments is that the observed speech in a live-talker scenario will undergo one acoustic impulse response (AIR) while there will be a pair of convolved AIRs in the replay scenario. We investigate how this physical fact may be used to detect a presentation attack. Drawing on established results in room acoustics, we show that the spectral standard deviation of an AIR is a promising feature for distinguishing between live and replayed speech. We develop a method based on convolutional neural networks (CNNs) to estimate the spectral standard deviation directly from a speech signal, leading to a zero-shot PAD approach. Several aspects of the detectability based on room acoustics alone are illustrated using data from ASVspoof2019 and ASVspoof2021.