  Mainstream deep learning-based dysarthric speech detection approaches
typically rely on processing the magnitude spectrum of the short-time Fourier
transform of input signals, while ignoring the phase spectrum. Although
considerable insight about the structure of a signal can be obtained from the
magnitude spectrum, the phase spectrum also contains inherent structures which
are not immediately apparent due to phase discontinuity. To reveal meaningful
phase structures, alternative phase representations such as the modified group
delay (MGD) and instantaneous frequency (IF) spectra have been investigated in
several applications. The objective of this paper is to investigate the
applicability of the unprocessed phase, MGD, and IF spectra for dysarthric
speech detection. Experimental results show that dysarthric cues are present in
all considered phase representations. Further, it is shown that using phase
representations as complementary features to the magnitude spectrum is
beneficial for deep learning-based dysarthric speech detection, with the
combination of magnitude and IF spectra yielding a high performance. The
presented results should raise awareness in the research community about the
potential of the phase spectrum for dysarthric speech detection and motivate
research into novel architectures which optimally exploit magnitude and phase
information.
