  We introduce sum-product networks (SPNs) for robust speech processing through
a simple robust automatic speaker identification (ASI) task. SPNs are deep
probabilistic graphical models capable of answering multiple probabilistic
queries. We show that SPNs are able to remain robust by using the marginal
probability density function (PDF) of the spectral features that reliably
represent speech. Though current SPN toolkits and learning algorithms are in
their infancy, we aim to show that SPNs have the potential to become a useful
tool for robust speech processing in the future. SPN speaker models are
evaluated here on real-world non-stationary and coloured noise sources at
multiple signal-to-noise ratio (SNR) levels. In terms of ASI accuracy, we find
that SPN speaker models are more robust than two recent convolutional neural
network (CNN)-based ASI systems. Additionally, SPN speaker models consist of
significantly fewer parameters than their CNN-based counterparts. The results
indicate that SPN speaker models could be a robust, parameter-efficient
alternative for ASI. Additionally, this work demonstrates that SPNs have
potential in related tasks, such as robust automatic speech recognition (ASR)
and automatic speaker verification (ASV). Availability: The SPN ASI system is
available at https://github.com/anicolson/SPN-ASI.
