Deep neural network based speech enhancement technique focuses on learning a noisy-to-clean transformation supervised by paired training data. However, the task-specific evaluation metric (e.g., PESQ) is usually non-differentiable and can not be directly constructed in the training criteria. This mismatch between the training objective and evaluation metric likely results in sub-optimal performance. To alleviate it, we propose a metric-oriented speech enhancement method (MOSE), which leverages the recent advances in the diffusion probabilistic model and integrates a metric-oriented training strategy into its reverse process. Specifically, we design an actor-critic based framework that considers the evaluation metric as a posterior reward, thus guiding the reverse process to the metric-increasing direction. The experimental results demonstrate that MOSE obviously benefits from metric-oriented training and surpasses the generative baselines in terms of all evaluation metrics.
翻译:基于深神经网络的语音增强技术侧重于学习由配对培训数据监督的噪音到清洁的转换,然而,任务特定评价指标(例如,PESQ)通常没有差别,不能直接在培训标准中构建。这种培训目标与评价指标之间不匹配,其效果可能低于最佳水平。为了减轻这种差异,我们提议了一种面向标准的语音增强方法(MOSE),该方法利用了传播概率模型的最新进展,并将一个面向标准的训练战略纳入反向进程。具体地说,我们设计了一个基于行为体的基于框架,将评价指标视为事后奖励,从而指导逆向进程走向量化增长的方向。实验结果表明,MOSE显然受益于面向指标的培训,并超越了所有评价指标的归因基线。