  The choice of an optimal time-frequency resolution is usually a difficult but
important step in tasks involving speech signal classification, e.g., speech
anti-spoofing. The variations of the performance with different choices of
timefrequency resolutions can be as large as those with different model
architectures, which makes it difficult to judge what the improvement actually
comes from when a new network architecture is invented and introduced as the
classifier. In this paper, we propose a multi-resolution front-end for feature
extraction in an end-to-end classification framework. Optimal weighted
combinations of multiple time-frequency resolutions will be learned
automatically given the objective of a classification task. Features extracted
with different time-frequency resolutions are weighted and concatenated as
inputs to the successive networks, where the weights are predicted by a
learnable neural network inspired by the weighting block in
squeeze-and-excitation networks (SENet). Furthermore, the refinement of the
chosen timefrequency resolutions is investigated by pruning the ones with
relatively low importance, which reduces the complexity and size of the model.
The proposed method is evaluated on the tasks of speech anti-spoofing in
ASVSpoof 2019 and its superiority has been justified by comparing with similar
baselines.
