  This paper presents a simple but effective method that uses multi-resolution
feature maps with convolutional neural networks (CNNs) for anti-spoofing in
automatic speaker verification (ASV). The central idea is to alleviate the
problem that the feature maps commonly used in anti-spoofing networks are
insufficient for building discriminative representations of audio segments, as
they are often extracted by a single-length sliding window. Resulting
trade-offs between time and frequency resolutions restrict the information in
single spectrograms. The proposed method improves both frequency resolution and
time resolution by stacking multiple spectrograms that are extracted using
different window lengths. These are fed into a convolutional neural network in
the form of multiple channels, making it possible to extract more information
from input signals while only marginally increasing computational costs. The
efficiency of the proposed method has been conformed on the ASVspoof 2019
database. We show that the use of the proposed multiresolution inputs
consistently outperforms that of score fusion across different CNN
architectures. Moreover, computational cost remains small.
