# Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement

arXiv: https://arxiv.org/abs/2106.04129 · HTML: https://mlsanity.com/paper/2106.04129/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2021.

## Built on by (most cited first, 12 of 12)

- [Real-Time Target Sound Extraction](https://mlsanity.com/paper/2211.02250/) (2022)
- [Personalized Speech Enhancement: New Models and Comprehensive Evaluation](https://mlsanity.com/paper/2110.09625/) (2021)
- [Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation](https://mlsanity.com/paper/2204.00771/) (2022)
- [Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables](https://mlsanity.com/paper/2311.00320/) (2023)
- [Look Once to Hear: Target Speech Hearing with Noisy Examples](https://mlsanity.com/paper/2405.06289/) (2024)
- [Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation](https://mlsanity.com/paper/2210.12635/) (2022)
- [Spatial Speech Translation: Translating Across Space With Binaural Hearables](https://mlsanity.com/paper/2504.18715/) (2025)
- [SVVAD: Personal Voice Activity Detection for Speaker Verification](https://mlsanity.com/paper/2305.19581/) (2023)
- [MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement](https://mlsanity.com/paper/2310.04369/) (2023)
- [Personalized Speech Enhancement Without a Separate Speaker Embedding Model](https://mlsanity.com/paper/2406.09928/) (2024)
- [Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation](https://mlsanity.com/paper/2211.02944/) (2022)
- [Contrastive Knowledge Distillation for Embedding Refinement in Personalized Speech Enhancement](https://mlsanity.com/paper/2601.16235/) (2026)

## Abstract (arXiv)

The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size and complexity. In this paper, we present Personalized PercepNet, a real-time speech enhancement model that separates a target speaker from a noisy multi-talker mixture without compromising on complexity of the recently proposed PercepNet. To enable speaker-dependent speech enhancement, we first show how we can train a perceptually motivated speaker embedder network to produce a representative embedding vector for the given speaker. Personalized PercepNet uses the target speaker embedding as additional information to pick out and enhance only the target speaker while suppressing all other competing sounds. Our experiments show that the proposed model significantly outperforms PercepNet and other baselines, both in terms of objective speech enhancement metrics and human opinion scores.