An Investigation of Spoofing Speech Detection Under Additive Noise and Reverberant Conditions

Spoofing detection for automatic speaker verification (ASV), which is to discriminate between live and artificial speech, has received increasing attentions recently. However, the previous studies have been done on the clean data without significant noise. It is still not clear whether the spoofing detectors trained on clean speech can generalise well under noisy conditions. In this work, we perform an investigation of spoofing detection under additive noise and reverberant conditions. In particular, we consider five difference additive noises at three different signal-to-noise ratios (SNR), and a reverberation noise with different reverberation time (RT). Our experimental results reveal that additive noises degrade the spoofing detectors trained on clean speech significantly. However, the reverberation does not hurt the performance too much.