  As speech processing systems in mobile and edge devices become more
commonplace, the demand for unintrusive speech quality monitoring increases.
Deep learning methods provide high-quality estimates of objective and
subjective speech quality metrics. However, their significant computational
requirements are often prohibitive on resource-constrained devices. To address
this issue, we investigated binary activation maps (BAMs) for speech quality
prediction on a convolutional architecture based on DNSMOS. We show that the
binary activation model with quantization aware training matches the predictive
performance of the baseline model. It further allows using other compression
techniques. Combined with 8-bit weight quantization, our approach results in a
25-fold memory reduction during inference, while replacing almost all dot
products with summations. Our findings show a path toward substantial resource
savings by supporting mixed-precision binary multiplication in hard- and
software.
