# Improving curriculum learning for target speaker extraction with synthetic speakers

arXiv: https://arxiv.org/abs/2410.00811 · HTML: https://mlsanity.com/paper/2410.00811/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2024.

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

- [PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction](https://mlsanity.com/paper/2607.08111/) (2026)

## Abstract (arXiv)

Target speaker extraction (TSE) aims to isolate individual speaker voices from complex speech environments. The effectiveness of TSE systems is often compromised when the speaker characteristics are similar to each other. Recent research has introduced curriculum learning (CL), in which TSE models are trained incrementally on speech samples of increasing complexity. In CL training, the model is first trained on samples with low speaker similarity between the target and interference speakers, and then on samples with high speaker similarity. To further improve CL, this paper uses a k-nearest neighbor-based voice conversion method to simulate and generate speech of diverse interference speakers, and then uses the generated data as part of the CL. Experiments demonstrate that training data based on synthetic speakers can effectively enhance the model's capabilities and significantly improve the performance of multiple TSE systems.