SECP: A Speech Enhancement-Based Curation Pipeline For Scalable Acquisition Of Clean Speech

2024/02/19 by Adam Sabra, Sabra, Adam, Cyprian Wronka +5 · 3 citations
Computer Science · #Speech Recognition and Synthesis #Speech and Audio Processing #Speech and dialogue systems

paper · pdf · doi:10.48550/arxiv.2402.12482

Abstract

As more speech technologies rely on a supervised deep learning approach with clean speech as the ground truth, a methodology to onboard said speech at scale is needed. However, this approach needs to minimize the dependency on human listening and annotation, only requiring a human-in-the-loop when needed. In this paper, we address this issue by outlining Speech Enhancement-based Curation Pipeline (SECP) which serves as a framework to onboard clean speech. This clean speech can then train a speech enhancement model, which can further refine the original dataset and thus close the iterative loop. By running two iterative rounds, we observe that enhanced output used as ground truth does not degrade model performance according to Δ_PESQ, a metric used in this paper. We also show through comparative mean opinion score (CMOS) based subjective tests that the highest and lowest bound of refined data is perceptually better than the original data.

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• MAGE: A Coarse-to-Fine Speech Enhancer with Masked Generative Model
• Improving Audio Classification by Transitioning from Zero- to Few-Shot

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