用神经网络评估语音质量，还能反过来优化语音生成与增强。
From Evaluation to Optimization: Neural Speech Assessment for Downstream Applications
- 用可微分的语音感知代理替代传统指标，指导语音模型优化。
- 无需干净参考信号，能真实场景下评估语音质量与可懂度。
- 适合语音增强、语音合成等下游任务，提升处理精度与效率。
语音质量评估一直是音频工程与语音科学的核心问题。尽管主观听感测试仍是评估感知质量与可懂度的黄金标准，但其高成本、耗时长且难以扩展，难以满足现代语音技术快速迭代的需求。传统客观指标虽计算高效，却依赖干净参考信号，属于侵入式方法，在实际应用中常不可行。近年来，大量基于神经网络的语音评估模型被提出，可有效预测质量与可懂度，表现优异。这些模型不仅用于评估，更逐步融入下游语音处理任务：一是作为可微分的感知代理，既评估又引导语音增强与合成模型的优化；二是识别关键语音特征，支持更精确高效的下游处理。本文综述了这两方面进展，指出当前局限，并展望未来研究方向，以推动语音评估在语音处理流程中的深度集成。
原文摘要 · Abstract (English)
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 rely on a clean reference signal, making them intrusive approaches. This presents a major limitation, as clean signals are often unavailable in real-world applications. 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.
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