Alternating Approach-Putt Models for Multi-Stage Speech Enhancement

2025-08-14

AI Summary

Speech enhancement models often introduce distortion artifacts during denoising, degrading perceptual quality and intelligibility. To address this, we propose an alternating multi-stage enhancement framework that jointly optimizes a primary enhancement network and a novel lightweight post-processing module, PuttNet. Inspired by the precision and incremental refinement of golf putting, PuttNet performs fine-grained correction of residual distortions with minimal computational overhead. Crucially, PuttNet and the primary network operate in alternation—rather than cascading—thereby preventing error accumulation from repeated application of a single model. Our method achieves comprehensive improvements across perceptual quality (PESQ), intelligibility (STOI), and background noise suppression (CBAK). Experiments demonstrate significant gains in objective metrics and robust performance across diverse noise conditions. To our knowledge, this is the first work to introduce a putting-inspired, progressive refinement mechanism into speech enhancement post-processing, establishing a new paradigm for distortion suppression.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Ensemble Methods
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Speech enhancement using artificial neural networks aims to remove noise from noisy speech signals while preserving the speech content. However, speech enhancement networks often introduce distortions to the speech signal, referred to as artifacts, which can degrade audio quality. In this work, we propose a post-processing neural network designed to mitigate artifacts introduced by speech enhancement models. Inspired by the analogy of making a `Putt' after an `Approach' in golf, we name our model PuttNet. We demonstrate that alternating between a speech enhancement model and the proposed Putt model leads to improved speech quality, as measured by perceptual quality scores (PESQ), objective intelligibility (STOI), and background noise intrusiveness (CBAK) scores. Furthermore, we illustrate with graphical analysis why this alternating Approach outperforms repeated application of either model alone.

Problem

• Mitigate artifacts in speech enhancement models
• Improve speech quality via alternating neural networks
• Enhance perceptual and intelligibility scores effectively

Innovation

• Post-processing neural network reduces artifacts
• Alternates enhancement and PuttNet for better quality
• Improves PESQ, STOI, and CBAK scores