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

arXiv: https://arxiv.org/abs/2402.12482 · HTML: https://mlsanity.com/paper/2402.12482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Hi-Fi Multi-Speaker English TTS Dataset](https://mlsanity.com/paper/2104.01497/) (2021)
- [A scalable noisy speech dataset and online subjective test framework](https://mlsanity.com/paper/1909.08050/) (2019)
- [A Fully Convolutional Neural Network for Speech Enhancement](https://mlsanity.com/paper/1609.07132/) (2016)
- [On training targets for noise-robust voice activity detection](https://mlsanity.com/paper/2102.07445/) (2021)
- [A non-causal FFTNet architecture for speech enhancement](https://mlsanity.com/paper/2006.04469/) (2020)

## Built on by (most cited first, 1 of 1)

- [Improving Audio Classification by Transitioning from Zero- to Few-Shot](https://mlsanity.com/paper/2507.20036/) (2025)

## Abstract (arXiv)

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 DeltaPESQ, 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.