  Conventional spoofing detection systems have heavily relied on the use of
handcrafted features derived from speech data. However, a notable shift has
recently emerged towards the direct utilization of raw speech waveforms, as
demonstrated by methods like SincNet filters. This shift underscores the demand
for more sophisticated audio sample features. Moreover, the success of deep
learning models, particularly those utilizing large pretrained wav2vec 2.0 as a
featurization front-end, highlights the importance of refined feature encoders.
In response, this research assessed the representational capability of wav2vec
2.0 as an audio feature extractor, modifying the size of its pretrained
Transformer layers through two key adjustments: (1) selecting a subset of
layers starting from the leftmost one and (2) fine-tuning a portion of the
selected layers from the rightmost one. We complemented this analysis with five
spoofing detection back-end models, with a primary focus on AASIST, enabling us
to pinpoint the optimal configuration for the selection and fine-tuning
process. In contrast to conventional handcrafted features, our investigation
identified several spoofing detection systems that achieve state-of-the-art
performance in the ASVspoof 2019 LA dataset. This comprehensive exploration
offers valuable insights into feature selection strategies, advancing the field
of spoofing detection.
