📝 SummarXiv

Abstract

The evaluation of synthetic and processed speech has long been a cornerstone of audio engineering and speech science. Although subjective listening tests remain the gold standard for assessing perceptual quality and intelligibility, their high cost, time requirements, and limited scalability present significant challenges in the rapid development cycles of modern speech technologies. Traditional objective metrics, while computationally efficient, often exhibit weak correlation with human perception, creating a perceptual gap between system optimization and actual user experience. Bridging this gap requires speech assessment models that are more closely aligned with human perception. In recent years, numerous neural network-based speech assessment models have been developed to predict quality and intelligibility, achieving promising results. Beyond their role in evaluation, these models are increasingly integrated into downstream speech processing tasks. This review focuses on their role in two main areas: (1) serving as differentiable perceptual proxies that not only assess but also guide the optimization of speech enhancement and synthesis models; and (2) enabling the detection of salient speech characteristics to support more precise and efficient downstream processing. Finally, we discuss current limitations and outline future research directions to further advance the integration of speech assessment into speech processing pipelines.