Title: DNN Filter Bank Cepstral Coefficients for Spoofing Detection,
Abstract: With the development of speech synthesis techniques, automatic speaker
verification systems face the serious challenge of spoofing attack. In order to
improve the reliability of speaker verification systems, we develop a new
filter bank based cepstral feature, deep neural network filter bank cepstral
coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The
deep neural network filter bank is automatically generated by training a filter
bank neural network (FBNN) using natural and synthetic speech. By adding
restrictions on the training rules, the learned weight matrix of FBNN is
band-limited and sorted by frequency, similar to the normal filter bank. Unlike
the manually designed filter bank, the learned filter bank has different filter
shapes in different channels, which can capture the differences between natural
and synthetic speech more effectively. The experimental results on the ASVspoof
{2015} database show that the Gaussian mixture model maximum-likelihood
(GMM-ML) classifier trained by the new feature performs better than the
state-of-the-art linear frequency cepstral coefficients (LFCC) based
classifier, especially on detecting unknown attacks.