  Deep learning-based techniques for automatic dysarthric speech detection have
recently attracted interest in the research community. State-of-the-art
techniques typically learn neurotypical and dysarthric discriminative
representations by processing time-frequency input representations such as the
magnitude spectrum of the short-time Fourier transform (STFT). Although these
techniques are expected to leverage perceptual dysarthric cues, representations
such as the magnitude spectrum of the STFT do not necessarily convey perceptual
aspects of complex sounds. Inspired by the temporal processing mechanisms of
the human auditory system, in this paper we factor signals into the product of
a slowly varying envelope and a rapidly varying fine structure. Separately
exploiting the different perceptual cues present in the envelope (i.e.,
phonetic information, stress, and voicing) and fine structure (i.e., pitch,
vowel quality, and breathiness), two discriminative representations are learned
through a convolutional neural network and used for automatic dysarthric speech
detection. Experimental results show that processing both the envelope and fine
structure representations yields a considerably better dysarthric speech
detection performance than processing only the envelope, fine structure, or
magnitude spectrum of the STFT representation.
