  Voice assistants, such as smart speakers, have exploded in popularity. It is
currently estimated that the smart speaker adoption rate has exceeded 35% in
the US adult population. Manufacturers have integrated speaker identification
technology, which attempts to determine the identity of the person speaking, to
provide personalized services to different members of the same family. Speaker
identification can also play an important role in controlling how the smart
speaker is used. For example, it is not critical to correctly identify the user
when playing music. However, when reading the user's email out loud, it is
critical to correctly verify the speaker that making the request is the
authorized user. Speaker verification systems, which authenticate the speaker
identity, are therefore needed as a gatekeeper to protect against various
spoofing attacks that aim to impersonate the enrolled user. This paper compares
popular learnable front-ends which learn the representations of audio by joint
training with downstream tasks (End-to-End). We categorize the front-ends by
defining two generic architectures and then analyze the filtering stages of
both types in terms of learning constraints. We propose replacing fixed
filterbanks with a learnable layer that can better adapt to anti-spoofing
tasks. The proposed FastAudio front-end is then tested with two popular
back-ends to measure the performance on the LA track of the ASVspoof 2019
dataset. The FastAudio front-end achieves a relative improvement of 27% when
compared with fixed front-ends, outperforming all other learnable front-ends on
this task.
