Towards an Interpretable Representation of Speaker Identity via
  Perceptual Voice Qualities
Robin Netzorg Bohan Yu Andrea Guzman Peter Wu Luna McNulty Gopala Anumanchipalli
Unlike other data modalities such as text and vision, speech does not lend
itself to easy interpretation. While lay people can understand how to describe
an image or sentence via perception, non-expert descriptions of speech often
end at high-level demographic information, such as gender or age. In this
paper, we propose a possible interpretable representation of speaker identity
based on perceptual voice qualities (PQs). By adding gendered PQs to the
pathology-focused Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V)
protocol, our PQ-based approach provides a perceptual latent space of the
character of adult voices that is an intermediary of abstraction between
high-level demographics and low-level acoustic, physical, or learned
representations. Contrary to prior belief, we demonstrate that these PQs are
hearable by ensembles of non-experts, and further demonstrate that the
information encoded in a PQ-based representation is predictable by various
speech representations.