  An emerging trend in audio processing is capturing low-level speech
representations from raw waveforms. These representations have shown promising
results on a variety of tasks, such as speech recognition and speech
separation. Compared to handcrafted features, learning speech features via
backpropagation provides the model greater flexibility in how it represents
data for different tasks theoretically. However, results from empirical study
shows that, in some tasks, such as voice spoof detection, handcrafted features
are more competitive than learned features. Instead of evaluating handcrafted
features and raw waveforms independently, this paper proposes an Auxiliary
Rawnet model to complement handcrafted features with features learned from raw
waveforms. A key benefit of the approach is that it can improve accuracy at a
relatively low computational cost. The proposed Auxiliary Rawnet model is
tested using the ASVspoof 2019 dataset and the results from this dataset
indicate that a light-weight waveform encoder can potentially boost the
performance of handcrafted-features-based encoders in exchange for a small
amount of additional computational work.
