  Speech enhancement models have greatly progressed in recent years, but still
show limits in perceptual quality of their speech outputs. We propose an
objective for perceptual quality based on temporal acoustic parameters. These
are fundamental speech features that play an essential role in various
applications, including speaker recognition and paralinguistic analysis. We
provide a differentiable estimator for four categories of low-level acoustic
descriptors involving: frequency-related parameters, energy or
amplitude-related parameters, spectral balance parameters, and temporal
features. Unlike prior work that looks at aggregated acoustic parameters or a
few categories of acoustic parameters, our temporal acoustic parameter (TAP)
loss enables auxiliary optimization and improvement of many fine-grain speech
characteristics in enhancement workflows. We show that adding TAPLoss as an
auxiliary objective in speech enhancement produces speech with improved
perceptual quality and intelligibility. We use data from the Deep Noise
Suppression 2020 Challenge to demonstrate that both time-domain models and
time-frequency domain models can benefit from our method.
