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

Shlomo E. Chazan, Jacob Goldberger, Sharon Gannot

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


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.1

Original languageEnglish
Pages (from-to)716-720
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Bibliographical note

Funding Information:
1This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 871245 and was supported by the Ministry of Science & Technology, Israel.

Publisher Copyright:
©2021 IEEE


  • Clustering
  • Mixture of experts


Dive into the research topics of 'Speech enhancement with mixture of deep experts with clean clustering pre-training'. Together they form a unique fingerprint.

Cite this