Metric-oriented Speech Enhancement using Diffusion Probabilistic Model

• URL: http://arxiv.org/abs/2302.11989v1
• Date: Thu, 23 Feb 2023 13:12:35 GMT
• Title: Metric-oriented Speech Enhancement using Diffusion Probabilistic Model
• Authors: Chen Chen, Yuchen Hu, Weiwei Weng, Eng Siong Chng
• Abstract summary: Deep neural network based speech enhancement technique focuses on learning a noisy-to-clean transformation supervised by paired training data. The task-specific evaluation metric (e.g., PESQ) is usually non-differentiable and can not be directly constructed in the training criteria. We propose a metric-oriented speech enhancement method (MOSE) which integrates a metric-oriented training strategy into its reverse process.
• Score: 23.84172431047342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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