# Speech enhancement with mixture-of-deep-experts with clean clustering pre-training

arXiv: https://arxiv.org/abs/2102.06034 · HTML: https://mlsanity.com/paper/2102.06034/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement](https://mlsanity.com/paper/1909.01019/) (2019)
- [Dynamic Noise Embedding: Noise Aware Training and Adaptation for Speech Enhancement](https://mlsanity.com/paper/2008.11920/) (2020)
- [A Hybrid Approach for Speech Enhancement Using MoG Model and Neural Network Phoneme Classifier](https://mlsanity.com/paper/1510.07315/) (2015)

## Built on by (most cited first, 4 of 4)

- [Efficient Personalized Speech Enhancement through Self-Supervised Learning](https://mlsanity.com/paper/2104.02017/) (2021)
- [Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection](https://mlsanity.com/paper/2105.03542/) (2021)
- [Socially Pertinent Robots in Gerontological Healthcare](https://mlsanity.com/paper/2404.07560/) (2024)
- [HybridSB-MoE: Dual-Domain Schr"odinger Bridges with Scene-Adaptive Expert Routing for Speech Enhancement](https://mlsanity.com/paper/2608.12715/) (2026)

## Abstract (arXiv)

In this study we present a mixture of deep experts (MoDE) neural-network architecture for single microphone speech enhancement. Our architecture comprises a set of deep neural networks (DNNs), each of which is an 'expert' in a different speech spectral pattern such as phoneme. A gating DNN is responsible for the latent variables which are the weights assigned to each expert's output given a speech segment. The experts estimate a mask from the noisy input and the final mask is then obtained as a weighted average of the experts' estimates, with the weights determined by the gating DNN. A soft spectral attenuation, based on the estimated mask, is then applied to enhance the noisy speech signal. As a byproduct, we gain reduction at the complexity in test time. We show that the experts specialization allows better robustness to unfamiliar noise types.