  Speaker identity plays a significant role in human communication and is being
increasingly used in societal applications, many through advances in machine
learning. Speaker identity perception is an essential cognitive phenomenon that
can be broadly reduced to two main tasks: recognizing a voice or discriminating
between voices. Several studies have attempted to identify acoustic correlates
of identity perception to pinpoint salient parameters for such a task. Unlike
other communicative social signals, most efforts have yielded inefficacious
conclusions. Furthermore, current neurocognitive models of voice identity
processing consider the bases of perception as acoustic dimensions such as
fundamental frequency, harmonics-to-noise ratio, and formant dispersion.
However, these findings do not account for naturalistic speech and
within-speaker variability. Representational spaces of current self-supervised
models have shown significant performance in various speech-related tasks. In
this work, we demonstrate that self-supervised representations from different
families (e.g., generative, contrastive, and predictive models) are
significantly better for speaker identification over acoustic representations.
We also show that such a speaker identification task can be used to better
understand the nature of acoustic information representation in different
layers of these powerful networks. By evaluating speaker identification
accuracy across acoustic, phonemic, prosodic, and linguistic variants, we
report similarity between model performance and human identity perception. We
further examine these similarities by juxtaposing the encoding spaces of models
and humans and challenging the use of distance metrics as a proxy for speaker
proximity. Lastly, we show that some models can predict brain responses in
Auditory and Language regions during naturalistic stimuli.
