Human subjective evaluation is the gold standard to evaluate speech quality optimized for human perception. Perceptual objective metrics serve as a proxy for subjective scores. The conventional and widely used metrics require a reference clean speech signal, which is unavailable in real recordings. The no-reference approaches correlate poorly with human ratings and are not widely adopted in the research community. One of the biggest use cases of these perceptual objective metrics is to evaluate noise suppression algorithms. This paper introduces a multi-stage self-teaching based perceptual objective metric that is designed to evaluate noise suppressors. The proposed method generalizes well in challenging test conditions with a high correlation to human ratings.

翻译：人类主观评价是评价为人类感知而优化的言语质量的黄金标准; 概念客观指标是主观分数的替代物; 传统和广泛使用的衡量标准需要参考清洁言语信号,这种信号在真实记录中是不存在的; 不参考方法与人类评级不相干,在研究界没有被广泛采用。 这些概念客观指标的最大使用案例之一是评价抑制噪音的算法。 本文介绍了一个多阶段的基于自我教学的观念客观指标,旨在评价噪音抑制器。 拟议的方法概括了挑战测试条件与人类评级高度相关的情况。