  A variety of recent works have looked into defenses for deep neural networks
against adversarial attacks particularly within the image processing domain.
Speech processing applications such as automatic speech recognition (ASR) are
increasingly relying on deep learning models, and so are also prone to
adversarial attacks. However, many of the defenses explored for ASR simply
adapt the image-domain defenses, which may not provide optimal robustness. This
paper explores speech specific defenses using the mel spectral domain, and
introduces a novel defense method called 'mel domain noise flooding' (MDNF).
MDNF applies additive noise to the mel spectrogram of a speech utterance prior
to re-synthesising the audio signal. We test the defenses against strong
white-box adversarial attacks such as projected gradient descent (PGD) and
Carlini-Wagner (CW) attacks, and show better robustness compared to a
randomized smoothing baseline across strong threat models.
