Since its inception, the field of deep speech enhancement has been dominated
by predictive (discriminative) approaches, such as spectral mapping or masking.
Recently, however, novel generative approaches have been applied to speech
enhancement, attaining good denoising performance with high subjective quality
scores. At the same time, advances in deep learning also allowed for the
creation of neural network-based metrics, which have desirable traits such as
being able to work without a reference (non-intrusively). Since generatively
enhanced speech tends to exhibit radically different residual distortions, its
evaluation using instrumental speech metrics may behave differently compared to
predictively enhanced speech. In this paper, we evaluate the performance of the
same speech enhancement backbone trained under predictive and generative
paradigms on a variety of metrics and show that intrusive and non-intrusive
measures correlate differently for each paradigm. This analysis motivates the
search for metrics that can together paint a complete and unbiased picture of
speech enhancement performance, irrespective of the model's training process.

研究发现，基于预测和生成两种模型训练的语音增强模型会对干扰和非干扰性能指标表现出不同的相关性，促使人们寻找更完整、不偏不倚的语音增强性能测量标准。

预测性和生成性环境下侵入式和非侵入式语音增强评估量的行为

On the Behavior of Intrusive and Non-intrusive Speech Enhancement  Metrics in Predictive and Generative Settings

Automatic evaluating the performance of Open-domain dialogue system is a
challenging problem. Recent work in neural network-based metrics has shown
promising opportunities for automatic dialogue evaluation. However, existing
methods mainly focus on monolingual evaluation, in which the trained metric is
not flexible enough to transfer across different languages. To address this
issue, we propose an adversarial multi-task neural metric (ADVMT) for
multi-lingual dialogue evaluation, with shared feature extraction across
languages. We evaluate the proposed model in two different languages.
Experiments show that the adversarial multi-task neural metric achieves a high
correlation with human annotation, which yields better performance than
monolingual ones and various existing metrics.

提出了一种跨语言对话评估的 ADVMT 模型，利用神经网络指标对开放域对话系统的表现进行评估，并在两种不同语言的情况下进行评估，结果显示其与人工评估的相关性高于现有指标。